Understanding drug‐likeness

A Tutorial on Support Vector Machines for Pattern Recognition

Burges, C. J. C. (1998) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009715923555 · Cited by 16,437 · see citing works

Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings

Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (1997) · Advanced Drug Delivery Reviews · doi:10.1016/s0169-409x(96)00423-1 · Cited by 11,484 · see citing works

Molecular properties that influence the oral bioavailability of drug candidates

Veber, D. F., Johnson, S. R., Cheng, H.-Y., Smith, B. R., Ward, K. W., & Kopple, K. D. (2002) · Journal of Medicinal Chemistry · doi:10.1021/jm020017n · Cited by 8,420 · see citing works

Escape from Flatland: Increasing Saturation as an Approach to Improving Clinical Success

Lovering, F., Bikker, J. A., & Humblet, C. C. (2009) · Journal of Medicinal Chemistry · doi:10.1021/jm901241e · Cited by 4,507 · see citing works

Drug-like properties and the causes of poor solubility and poor permeability

Lipinski, C. A. (2000) · Journal of Pharmacological and Toxicological Methods · doi:10.1016/s1056-8719(00)00107-6 · Cited by 3,776 · see citing works

The problem of overfitting.

Hawkins, D. M. (2004) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci0342472 · Cited by 2,522 · see citing works

The influence of drug-like concepts on decision-making in medicinal chemistry

Leeson, P. D., & Springthorpe, B. (2007) · Nature Reviews Drug Discovery · doi:10.1038/nrd2445 · Cited by 2,403 · see citing works

ADMET in silico modelling: towards prediction paradise?

Waterbeemd, H. V. D., & Gifford, E. (2003) · Nature Reviews Drug Discovery · doi:10.1038/nrd1032 · Cited by 1,894 · see citing works

Simple Selection Criteria for Drug-like Chemical Matter

Muegge, I., Heald, S. L., & Brittelli, D. R. (2001) · Journal of Medicinal Chemistry · doi:10.1021/jm015507e · Cited by 1,351 · see citing works

Medicinal Chemistry and the Molecular Operating Environment (MOE): Application of QSAR and Molecular Docking to Drug Discovery

Vilar, S., Cozza, G., & Moro, S. (2008) · Current Topics in Medicinal Chemistry · doi:10.2174/156802608786786624 · Cited by 1,200 · see citing works

Encyclopedia of computational chemistry

(1999) · Choice Reviews Online · doi:10.5860/choice.36-5691 · Cited by 1,148 · see citing works

Moving beyond Rules: The Development of a Central Nervous System Multiparameter Optimization (CNS MPO) Approach To Enable Alignment of Druglike Properties

Wager, T. T., Hou, X., Verhoest, P. R., & Villalobos, A. (2010) · ACS Chemical Neuroscience · doi:10.1021/cn100008c · Cited by 1,113 · see citing works

A Bioavailability Score

Martin, Y. C. (2005) · Journal of Medicinal Chemistry · doi:10.1021/jm0492002 · Cited by 1,042 · see citing works

Virtual screening—an overview

Walters, W. P., Stahl, M. T., & Murcko, M. A. (1998) · Drug Discovery Today · doi:10.1016/s1359-6446(97)01163-x · Cited by 996 · see citing works

The impact of aromatic ring count on compound developability – are too many aromatic rings a liability in drug design?

Ritchie, T. J., & Macdonald, S. J. F. (2009) · Drug Discovery Today · doi:10.1016/j.drudis.2009.07.014 · Cited by 913 · see citing works

Molecular Complexity and Its Impact on the Probability of Finding Leads for Drug Discovery

Hann, M. M., Leach, A. R., & Harper, G. (2001) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci000403i · Cited by 882 · see citing works

970 Million Druglike Small Molecules for Virtual Screening in the Chemical Universe Database GDB-13

Blum, L. C., & Reymond, J. (2009) · Journal of the American Chemical Society · doi:10.1021/ja902302h · Cited by 874 · see citing works

Is There a Difference between Leads and Drugs? A Historical Perspective

Oprea, T. I., Davis, A. M., Teague, S. J., & Leeson, P. D. (2001) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci010366a · Cited by 776 · see citing works

Calculating log Poct from structures

Leo, A. J. (1993) · Chemical Reviews · doi:10.1021/cr00020a001 · Cited by 705 · see citing works

The Practice of Medicinal Chemistry

(2003) · Elsevier eBooks · doi:10.1016/b978-0-12-744481-9.x5000-4 · Cited by 702 · see citing works

Neural network studies. 1. Comparison of overfitting and overtraining

Tetko, I. V., Livingstone, D. J., & Luik, A. I. (1995) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci00027a006 · Cited by 670 · see citing works

The influence of lead discovery strategies on the properties of drug candidates

Keserű, G. M., & Makara, G. M. (2009) · Nature Reviews Drug Discovery · doi:10.1038/nrd2796 · Cited by 641 · see citing works

Rapid calculation of polar molecular surface area and its application to the prediction of transport phenomena. 1. Prediction of intestinal absorption

Clark, D. E. (1999) · Journal of Pharmaceutical Sciences · doi:10.1021/js9804011 · Cited by 632 · see citing works

Property distribution of drug-related chemical databases*

Oprea, T. I. (2000) · Journal of Computer-Aided Molecular Design · doi:10.1023/a:1008130001697 · Cited by 629 · see citing works

A Comparison of Physiochemical Property Profiles of Development and Marketed Oral Drugs

Wenlock, M. C., Austin, R. P., Barton, P., Davis, A. M., & Leeson, P. D. (2003) · Journal of Medicinal Chemistry · doi:10.1021/jm021053p · Cited by 552 · see citing works

Functional group contributions to drug-receptor interactions

Andrews, P. R., Craik, D. J., & Martin, J. L. (1984) · Journal of Medicinal Chemistry · doi:10.1021/jm00378a021 · Cited by 538 · see citing works

Rapid calculation of polar molecular surface area and its application to the prediction of transport phenomena. 2. Prediction of blood–brain barrier penetration

Clark, D. E. (1999) · Journal of Pharmaceutical Sciences · doi:10.1021/js980402t · Cited by 523 · see citing works

Pursuing the leadlikeness concept in pharmaceutical research

Hann, M. M., & Oprea, T. I. (2004) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2004.04.003 · Cited by 516 · see citing works

Can We Learn To Distinguish between “Drug-like” and “Nondrug-like” Molecules?

Ajay, Walters, W. P., & Murcko, M. A. (1998) · Journal of Medicinal Chemistry · doi:10.1021/jm970666c · Cited by 484 · see citing works

Selection criteria for drug‐like compounds

Muegge, I. (2003) · Medicinal Research Reviews · doi:10.1002/med.10041 · Cited by 418 · see citing works

A Scoring Scheme for Discriminating between Drugs and Nondrugs

Sadowski, J., & Kubinyi, H. (1998) · Journal of Medicinal Chemistry · doi:10.1021/jm9706776 · Cited by 392 · see citing works

Characteristic Physical Properties and Structural Fragments of Marketed Oral Drugs

Vieth, M., Siegel, M. G., Higgs, R. E., Watson, I. A., Robertson, D. H., Savin, K. A., Durst, G. L., & Hipskind, P. A. (2003) · Journal of Medicinal Chemistry · doi:10.1021/jm030267j · Cited by 360 · see citing works

Drug research: myths, hype and reality

Kubinyi, H. (2003) · Nature Reviews Drug Discovery · doi:10.1038/nrd1156 · Cited by 348 · see citing works

Similarity Searching of Chemical Databases Using Atom Environment Descriptors (MOLPRINT 2D):  Evaluation of Performance

Bender, A., Mussa, H. Y., Glen, R. C., & Reiling, S. (2004) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci0498719 · Cited by 322 · see citing works

QSAR Model for Drug Human Oral Bioavailability

Yoshida, F., & Topliss, J. G. (2000) · Journal of Medicinal Chemistry · doi:10.1021/jm0000564 · Cited by 302 · see citing works

Drugs of natural origin : a textbook of pharmacognosy

Samuelsson, G. (1999) · Medical Entomology and Zoology · Cited by 286 · see citing works

Novel Chemical Space Exploration via Natural Products

Rosén, J., Gottfries, J., Mureşan, S., Backlund, A., & Oprea, T. I. (2009) · Journal of Medicinal Chemistry · doi:10.1021/jm801514w · Cited by 284 · see citing works

Assessing drug-likeness – what are we missing?

Vistoli, G., Pedretti, A., & Testa, B. (2008) · Drug Discovery Today · doi:10.1016/j.drudis.2007.11.007 · Cited by 279 · see citing works

Comparison of topological descriptors for similarity-based virtual screening using multiple bioactive reference structures

Hert, J., Willett, P., Wilton, D. J., Acklin, P., Azzaoui, K., Jacoby, E., & Schuffenhauer, A. (2004) · Organic & Biomolecular Chemistry · doi:10.1039/b409865j · Cited by 261 · see citing works

Drugs, leads, and drug-likeness: an analysis of some recently launched drugs

Proudfoot, J. (2002) · Bioorganic & Medicinal Chemistry Letters · doi:10.1016/s0960-894x(02)00244-5 · Cited by 252 · see citing works

Integrating virtual screening in lead discovery

Oprea, T. I., & Matter, H. (2004) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2004.06.008 · Cited by 251 · see citing works

Absorption Classification of Oral Drugs Based on Molecular Surface Properties

Bergström, C. A. S., Strafford, M., Lazorova, L., Avdeef, A., Luthman, K., & Artursson, P. (2003) · Journal of Medicinal Chemistry · doi:10.1021/jm020986i · Cited by 248 · see citing works

Molecular Similarity Searching Using Atom Environments, Information-Based Feature Selection, and a Naïve Bayesian Classifier

Bender, A., Mussa, H. Y., Glen, R. C., & Reiling, S. (2003) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci034207y · Cited by 246 · see citing works

Analogue‐based Drug Discovery

(2006) · Wiley eBooks · doi:10.1002/3527608001 · Cited by 242 · see citing works

The Signature Molecular Descriptor. 1. Using Extended Valence Sequences in QSAR and QSPR Studies

Faulon, J., Visco, D. P., & Pophale, R. S. (2003) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci020345w · Cited by 234 · see citing works

Drug Discovery Using Support Vector Machines. The Case Studies of Drug-likeness, Agrochemical-likeness, and Enzyme Inhibition Predictions

Zernov, V. V., Balakin, K. V., Ivaschenko, A. A., Savchuk, N., & Плетнев, И. В. (2003) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci0340916 · Cited by 211 · see citing works

Drug discovery in jeopardy

Cuatrecasas, P. (2006) · Journal of Clinical Investigation · doi:10.1172/jci29999 · Cited by 209 · see citing works

Evaluation of Dynamic Polar Molecular Surface Area as Predictor of Drug Absorption: Comparison with Other Computational and Experimental Predictors

Palm, K., Luthman, K., Ungell, A.-L. B., Strandlund, G., Beigi, F., Lundahl, P., & Artursson, P. (1998) · Journal of Medicinal Chemistry · doi:10.1021/jm980313t · Cited by 206 · see citing works

Application of ALOGPS to predict 1‐octanol/water distribution coefficients, logP, and logD, of AstraZeneca in‐house database

Tetko, I. V., & Bruneau, P. (2004) · Journal of Pharmaceutical Sciences · doi:10.1002/jps.20217 · Cited by 204 · see citing works

Current trends in lead discovery: Are we looking for the appropriate properties?

Oprea, T. I. (2002) · Journal of Computer-Aided Molecular Design · doi:10.1023/a:1020877402759 · Cited by 193 · see citing works

An Analysis of the Binding Efficiencies of Drugs and Their Leads in Successful Drug Discovery Programs

Perola, E. (2010) · Journal of Medicinal Chemistry · doi:10.1021/jm100118x · Cited by 189 · see citing works

ADME Evaluation in Drug Discovery. 6. Can Oral Bioavailability in Humans Be Effectively Predicted by Simple Molecular Property-Based Rules?

Hou, T., Wang, J., Zhang, W., & Xu, X. (2007) · Journal of Chemical Information and Modeling · doi:10.1021/ci6003515 · Cited by 179 · see citing works

Innovation in the pharmaceutical industry

Horrobin, D. F. (2000) · Journal of the Royal Society of Medicine · doi:10.1177/014107680009300702 · Cited by 173 · see citing works

The Signature Molecular Descriptor. 2. Enumerating Molecules from Their Extended Valence Sequences

Faulon, J., Churchwell, C. J., & Visco, D. P. (2003) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci020346o · Cited by 151 · see citing works

Discriminating between Drugs and Nondrugs by Prediction of Activity Spectra for Substances (PASS)

Anzali, S., Barnickel, G., Cezanne, B., Krug, M., Filimonov, D. A., & Poroikov, V. (2001) · Journal of Medicinal Chemistry · doi:10.1021/jm0010670 · Cited by 146 · see citing works

Computational Medicinal Chemistry for Drug Discovery

Matter, H. (2004) · Drug Discovery Today · doi:10.1016/s1359-6446(04)03071-5 · Cited by 143 · see citing works

Incorporation of Magnetic Nanoparticles in New Hybrid Networks Based on Heteropolyanions and Polyacrylamide

Mayer, C. R., Cabuil, V., Lalot, T., & Thouvenot, R. (1999) · Angewandte Chemie International Edition · doi:10.1002/(sici)1521-3773(19991216)38:24 · Cited by 139 · see citing works

Potential Drugs and Nondrugs: Prediction and Identification of Important Structural Features

Wagener, M., & van Geerestein, V. J. (2000) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci990266t · Cited by 138 · see citing works

Substructural analysis. Novel approach to the problem of drug design

CRAMER, R. D. I., Redl, G., & Berkoff, C. E. (1974) · Journal of Medicinal Chemistry · doi:10.1021/jm00251a014 · Cited by 135 · see citing works

Improving the Odds in Discriminating “Drug-like” from “Non Drug-like” Compounds

Frimurer, T. M., Bywater, R. P., Nærum, L., Nørskov‐Lauritsen, L., & Brunak, S. (2000) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci0003810 · Cited by 129 · see citing works

Tautomerism in Computer‐Aided Drug Design

Pospíšil, P., Ballmer, P., Scapozza, L., & Folkers, G. (2003) · Journal of Receptors and Signal Transduction · doi:10.1081/rrs-120026975 · Cited by 122 · see citing works

Descriptors, Physical Properties, and Drug-Likeness

Brüstle, M., Beck, B., Schindler, T., King, W., Mitchell, T. J., & Clark, T. (2002) · Journal of Medicinal Chemistry · doi:10.1021/jm011027b · Cited by 121 · see citing works

Predicting Caco-2 Cell Permeation Coefficients of Organic Molecules Using Membrane-Interaction QSAR Analysis

Kulkarni, A., Han, Y., & Hopfinger, A. J. (2002) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci010108d · Cited by 116 · see citing works

Lead-like, drug-like or “Pub-like”: how different are they?

Oprea, T. I., Allu, T. K., Fara, D. C., Curpan, R. F., Ostopovici, L., & Bologa, C. (2007) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-007-9105-3 · Cited by 114 · see citing works

Development, interpretation and temporal evaluation of a global QSAR of hERG electrophysiology screening data

Gavaghan, C. L., Arnby, C. H., Blomberg, N., Strandlund, G., & Boyer, S. K. (2007) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-006-9095-6 · Cited by 101 · see citing works

Blood–brain barrier permeability considerations for CNS-targeted compound library design

Hitchcock, S. A. (2008) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2008.03.019 · Cited by 100 · see citing works

Development of a Method for Evaluating Drug-Likeness and Ease of Synthesis Using a Data Set in Which Compounds Are Assigned Scores Based on Chemists' Intuition

Takaoka, Y., Endo, Y., Yamanobe, S., Kakinuma, H., Okubo, T., Shimazaki, Y., Ota, T., Sumiya, S., & Yoshikawa, K. (2003) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci034043l · Cited by 97 · see citing works

From Narcosis to Hyperspace: The History of QSAR

Kubinyi, H. (2002) · Quantitative Structure-Activity Relationships · doi:10.1002/1521-3838(200210)21:4 · Cited by 94 · see citing works

The Signature Molecular Descriptor. 4. Canonizing Molecules Using Extended Valence Sequences

Faulon, J., Collins, M. J., & Carr, R. D. (2004) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci0341823 · Cited by 94 · see citing works

Chemical space navigation in lead discovery

Oprea, T. I. (2002) · Current Opinion in Chemical Biology · doi:10.1016/s1367-5931(02)00329-0 · Cited by 87 · see citing works

Rapid Evaluation of Synthetic and Molecular Complexity for in Silico Chemistry

Allu, T. K., & Oprea, T. I. (2005) · Journal of Chemical Information and Modeling · doi:10.1021/ci0501387 · Cited by 87 · see citing works

Classifying ‘Drug-likeness' with Kernel-Based Learning Methods

Müller, K., Rätsch, G., Sonnenburg, S., Mika, S., Grimm, M., & Heinrich, N. (2005) · Journal of Chemical Information and Modeling · doi:10.1021/ci049737o · Cited by 85 · see citing works

Drug Discovery Handbook

(2005) · Wiley eBooks · doi:10.1002/0471728780 · Cited by 83 · see citing works

Drug prototypes and their exploitation

(1997) · General Pharmacology The Vascular System · doi:10.1016/s0306-3623(97)90084-7 · Cited by 78 · see citing works

A New Rapid and Effective Chemistry Space Filter in Recognizing a Druglike Database

Zheng, S., Luo, X., Chen, G., Zhu, W., Shen, J., Chen, K., & Jiang, H. (2005) · Journal of Chemical Information and Modeling · doi:10.1021/ci050031j · Cited by 72 · see citing works

Strategies for Compound Selection

Olah, M., Bologa, C., & Oprea, T. I. (2004) · Current Drug Discovery Technologies · doi:10.2174/1570163043334965 · Cited by 69 · see citing works

A statistical-heuristic method for automated selection of drugs for screening

Hodes, L., HAZARD, G. F. J., Geran, R. I., & Richman, S. (1977) · Journal of Medicinal Chemistry · doi:10.1021/jm00214a002 · Cited by 68 · see citing works

Gradual in Silico Filtering for Druglike Substances

Schneider, N., Jäckels, C., Andrés, C., & Hutter, M. C. (2008) · Journal of Chemical Information and Modeling · doi:10.1021/ci700351y · Cited by 61 · see citing works

Chemical information management in drug discovery: optimizing the computational and combinatorial chemistry interfaces

Oprea, T. I., Gottfries, J., Sherbukhin, V., Svensson, P., & Kühler, T. C. (2000) · Journal of Molecular Graphics and Modelling · doi:10.1016/s1093-3263(00)00066-8 · Cited by 57 · see citing works

Toward Designing Drug-Like Libraries: A Novel Computational Approach for Prediction of Drug Feasibility of Compounds

Wang, J., & Ramnarayan, K. (1999) · Journal of Combinatorial Chemistry · doi:10.1021/cc990032m · Cited by 55 · see citing works

Innovation deficit revisited: reflections on the productivity of pharmaceutical R&D

Drews, J. (1998) · Drug Discovery Today · doi:10.1016/s1359-6446(98)01252-5 · Cited by 55 · see citing works

A Sorcerer's apprentice and The Rule of Five: from rule-of-thumb to commandment and beyond

Abad‐Zapatero, C. (2007) · Drug Discovery Today · doi:10.1016/j.drudis.2007.10.022 · Cited by 53 · see citing works

A Large Descriptor Set and a Probabilistic Kernel-Based Classifier Significantly Improve Druglikeness Classification

Li, Q., Bender, A., Pei, J., & Lai, L. (2007) · Journal of Chemical Information and Modeling · doi:10.1021/ci700107y · Cited by 49 · see citing works

Use of Classification Regression Tree in Predicting Oral Absorption in Humans

Bai, J. P. F., Utis, A., Crippen, G. M., He, H., Fischer, V., Tullman, R. H., Yin, H., Hsu, C., Jiang, L., & Hwang, K. (2004) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci040023n · Cited by 47 · see citing works

Antitargets

(2008) · Methods and principles in medicinal chemistry · doi:10.1002/9783527621460 · Cited by 44 · see citing works

Scaffold Topologies. 2. Analysis of Chemical Databases

Wester, M. J., Pollock, S. N., Coutsias, E. A., Allu, T. K., Mureşan, S., & Oprea, T. I. (2008) · Journal of Chemical Information and Modeling · doi:10.1021/ci700342h · Cited by 44 · see citing works

Scaffold Topologies. 1. Exhaustive Enumeration up to Eight Rings

Pollock, S. N., Coutsias, E. A., Wester, M. J., & Oprea, T. I. (2008) · Journal of Chemical Information and Modeling · doi:10.1021/ci7003412 · Cited by 44 · see citing works

A stochastic algorithm for global optimization and for best populations: A test case of side chains in proteins

Glick, M., Rayan, A., & Goldblum, A. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.022418199 · Cited by 44 · see citing works

Progress in Drug Research

Jucker, E. (1985) · Birkhäuser Basel eBooks · doi:10.1007/978-3-0348-9315-2 · Cited by 40 · see citing works

Model-Free Drug-Likeness from Fragments

Ursu, O., & Oprea, T. I. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci100202p · Cited by 37 · see citing works

Transporter‐Mediated Efflux Influences CNS Side Effects: ABCB1, from Antitarget to Target

Broccatelli, F., Carosati, E., Cruciani, G., & Oprea, T. I. (2010) · Molecular Informatics · doi:10.1002/minf.200900075 · Cited by 34 · see citing works

Exploring the conformational space of cyclic peptides by a stochastic search method

Rayan, A., Senderowitz, H., & Goldblum, A. (2004) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2003.12.012 · Cited by 33 · see citing works

Drug Discovery and Development

(2006) · Wiley eBooks · doi:10.1002/0471780103 · Cited by 32 · see citing works

Statistical thinking and technique for QSAR and related studies. Part I: General theory

Stone, M., & Jonathan, P. (1993) · Journal of Chemometrics · doi:10.1002/cem.1180070603 · Cited by 31 · see citing works

Predicting Oral Druglikeness by Iterative Stochastic Elimination

Rayan, A., Marcus, D., & Goldblum, A. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci9004354 · Cited by 29 · see citing works

Pharmaceutical Data Mining: Approaches and Applications for Drug Discovery

Balakin, K. V. (2009) · Cited by 27 · see citing works

Pharmacophore Features Distributions in Different Classes of Compounds

Zuccotto, F. (2003) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci034068k · Cited by 24 · see citing works

Are There Differences between Launched Drugs, Clinical Candidates, and Commercially Available Compounds?

Ohno, K., Nagahara, Y., Tsunoyama, K., & Orita, M. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci100023s · Cited by 17 · see citing works

Drugs and Drug-Like Compounds: Discriminating Approved Pharmaceuticals from Screening-Library Compounds

Schierz, A. C., & King, R. D. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04031-3_29 · Cited by 4 · see citing works

A BOILED‐Egg To Predict Gastrointestinal Absorption and Brain Penetration of Small Molecules

Daina, A., & Zoete, V. (2016) · ChemMedChem · doi:10.1002/cmdc.201600182 · Cited by 2,637 · see citing works

Quantifying the chemical beauty of drugs

Bickerton, G. R., Paolini, G. V., Besnard, J., Mureşan, S., & Hopkins, A. L. (2012) · Nature Chemistry · doi:10.1038/nchem.1243 · Cited by 2,301 · see citing works

Computational Methods in Drug Discovery

Sliwoski, G. R., Kothiwale, S., Meiler, J., & Lowe, E. W. (2013) · Pharmacological Reviews · doi:10.1124/pr.112.007336 · Cited by 2,226 · see citing works

ADMET-score – a comprehensive scoring function for evaluation of chemical drug-likeness

Guan, L., Yang, H., Cai, Y., Sun, L., Di, P., Li, W., Liu, G., & Tang, Y. (2018) · MedChemComm · doi:10.1039/c8md00472b · Cited by 745 · see citing works

A drug-likeness toolbox facilitates ADMET study in drug discovery

Jia, C., Li, J., Hao, G., & Yang, G. (2019) · Drug Discovery Today · doi:10.1016/j.drudis.2019.10.014 · Cited by 556 · see citing works

The application of in silico drug-likeness predictions in pharmaceutical research

Tian, S., Wang, J., Li, Y., Li, D., Xu, L., & Hou, T. (2015) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2015.01.009 · Cited by 509 · see citing works

Two Decades under the Influence of the Rule of Five and the Changing Properties of Approved Oral Drugs.

Shultz, M. (2018) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.8b00686 · Cited by 375 · see citing works

High-Throughput Screening: today’s biochemical and cell-based approaches

Blay, V., Tolani, B., Ho, S. P., & Arkin, M. R. (2020) · Drug Discovery Today · doi:10.1016/j.drudis.2020.07.024 · Cited by 404 · see citing works

Chemical predictive modelling to improve compound quality

Cumming, J. G., Davis, A. M., Mureşan, S., Haeberlein, M., & Chen, H. (2013) · Nature Reviews Drug Discovery · doi:10.1038/nrd4128 · Cited by 309 · see citing works

Going further than Lipinski's rule in drug design

Walters, W. P. (2012) · Expert Opinion on Drug Discovery · doi:10.1517/17460441.2012.648612 · Cited by 313 · see citing works

Improving Drug Design: An Update on Recent Applications of Efficiency Metrics, Strategies for Replacing Problematic Elements, and Compounds in Nontraditional Drug Space

Meanwell, N. A. (2016) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.6b00043 · Cited by 244 · see citing works

Artificial Intelligence for Drug Discovery: Are We There Yet?

Hasselgren, C., & Oprea, T. I. (2024) · Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-040323-040828 · Cited by 236 · see citing works

Molecular inflation, attrition and the rule of five

Leeson, P. D. (2016) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2016.01.018 · Cited by 182 · see citing works

Glossary of terms used in computational drug design, part II (IUPAC Recommendations 2015)

Martin, Y. C., Abagyan, R. A., Ferenczy, G. G., Gillet, V. J., Oprea, T. I., Ulander, J., Winkler, D. A., & Zefirov, N. S. (2016) · Pure and Applied Chemistry · doi:10.1515/pac-2012-1204 · Cited by 161 · see citing works

Will Artificial Intelligence for Drug Discovery Impact Clinical Pharmacology?

Zhavoronkov, A., Vanhaelen, Q., & Oprea, T. I. (2020) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1795 · Cited by 159 · see citing works

Structure-based ligand discovery for the protein–protein interface of chemokine receptor CXCR4

Mysinger, M. M., Weiss, D. R., Ziarek, J. J., Gravel, S., Doak, A. K., Karpiak, J. D., Heveker, N., Shoichet, B. K., & Volkman, B. F. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1120431109 · Cited by 129 · see citing works

Virtual Screening Techniques in Drug Discovery: Review and Recent Applications

Rocha, S. F. L. D. S., Olanda, C. G., Fokoue, H. H., & Sant’Anna, C. M. R. (2019) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026619666190816101948 · Cited by 124 · see citing works

A survey of generative AI for de novo drug design: new frontiers in molecule and protein generation

Tang, X., Dai, H., Knight, E., Wu, F., Li, Y., Li, T., & Gerstein, M. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae338 · Cited by 126 · see citing works

Synergistic potential of Citrus aurantium L. essential oil with antibiotics against Candida albicans

Nidhi, P., Rolta, R., Kumar, V., Dev, K., & Sourirajan, A. (2020) · Journal of Ethnopharmacology · doi:10.1016/j.jep.2020.113135 · Cited by 101 · see citing works

Niclosamide repositioning for treating cancer: Challenges and nano-based drug delivery opportunities

Barbosa, E. J., Löbenberg, R., de Araújo, G. L. B., & Bou‐Chacra, N. A. (2019) · European Journal of Pharmaceutics and Biopharmaceutics · doi:10.1016/j.ejpb.2019.05.004 · Cited by 99 · see citing works

PerMM: A Web Tool and Database for Analysis of Passive Membrane Permeability and Translocation Pathways of Bioactive Molecules

Lomize, A. L., Hage, J. M., Schnitzer, K. A., Golobokov, K., LaFaive, M. B., Forsyth, A. C., & Pogozheva, I. D. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00225 · Cited by 97 · see citing works

Structure Based Docking and Molecular Dynamic Studies of Plasmodial Cysteine Proteases against a South African Natural Compound and its Analogs

Musyoka, T. M., Kanzi, A. M., Lobb, K. A., & Bishop, Ö. T. (2016) · Scientific Reports · doi:10.1038/srep23690 · Cited by 94 · see citing works

Absorption matters: A closer look at popular oral bioavailability rules for drug approvals

Soares, A. C. G., Sousa, G. H. M., Calil, R. L., & Trossini, G. H. G. (2023) · Molecular Informatics · doi:10.1002/minf.202300115 · Cited by 87 · see citing works

How drug-like are ‘ugly’ drugs: do drug-likeness metrics predict ADME behaviour in humans?

Ritchie, T. J., & Macdonald, S. J. F. (2014) · Drug Discovery Today · doi:10.1016/j.drudis.2014.01.007 · Cited by 86 · see citing works

Drug/nondrug classification using Support Vector Machines with various feature selection strategies

Korkmaz, S., Zararsız, G., & Göksülük, D. (2014) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2014.08.009 · Cited by 86 · see citing works

Structure-based design of new diclofenac: Physicochemical, spectral, molecular docking, dynamics simulation and ADMET studies

Uzzaman, M., Hasan, M. K., Mahmud, S., Fatema, K., & Matin, M. M. (2021) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2021.100677 · Cited by 80 · see citing works

A Study on Cheminformatics and its Applications on Modern Drug Discovery

Begam, B. F., & Kumar, J. S. (2012) · Procedia Engineering · doi:10.1016/j.proeng.2012.06.156 · Cited by 79 · see citing works

Frameworks for targeting RNA with small molecules

Juru, A. U., & Hargrove, A. E. (2021) · Journal of Biological Chemistry · doi:10.1074/jbc.rev120.015203 · Cited by 75 · see citing works

Quercetin as a potential treatment for COVID-19-induced acute kidney injury: Based on network pharmacology and molecular docking study

Gu, Y.-Y., Zhang, M., Cen, H., Wu, Y.-F., Lu, Z., Lu, F., Liu, X.-S., & Lan, H.-Y. (2021) · PLOS ONE · doi:10.1371/journal.pone.0245209 · Cited by 75 · see citing works

Virtual Screening Strategies in Medicinal Chemistry: The State of the Art and Current Challenges

Braga, R. C., Alves, V. M., Silva, A. C., do Nascimento, M. N., Silva, F. M., Lião, L. M., & Andrade, C. H. (2014) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026614666140929120749 · Cited by 71 · see citing works

All-hydrocarbon stapled peptides as Synthetic Cell-Accessible Mini-Proteins

Verdine, G. L., & Hilinski, G. J. (2012) · Drug Discovery Today Technologies · doi:10.1016/j.ddtec.2012.01.004 · Cited by 70 · see citing works

Beyond the Flavour: The Potential Druggability of Chemosensory G Protein-Coupled Receptors

Pizio, A. D., Behrens, M., & Krautwurst, D. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20061402 · Cited by 69 · see citing works

Pyrazole–coumarin and pyrazole–quinoline chalcones as potential antitubercular agents

Kumar, G., Krishna, V. S., Sriram, D., & Jachak, S. M. (2020) · Archiv der Pharmazie · doi:10.1002/ardp.202000077 · Cited by 68 · see citing works

DrugMetric: quantitative drug-likeness scoring based on chemical space distance

Li, B., Wang, Z., Liu, Z., Tao, Y., Sha, C., He, M., & Li, X. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae321 · Cited by 74 · see citing works

Synthesis and Biological Evaluation of N,N′-Squaramides with High in Vivo Efficacy and Low Toxicity: Toward a Low-Cost Drug against Chagas Disease

Olmo, F., Rotger, C., Ramírez-Macías, I., Martínez‐Crespo, L., Marı́n, C., Carreras, L., Urbanová, K., Vega, M., Chaves-Lemaur, G., Sampedro, Á., et al. (2014) · Journal of Medicinal Chemistry · doi:10.1021/jm4017015 · Cited by 66 · see citing works

Structural Insights into the Molecular Basis of the Ligand Promiscuity

Sturm, N., Desaphy, J., Quinn, R. J., Rognan, D., & Kellenberger, E. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci300196g · Cited by 63 · see citing works

Computational modeling of human oral bioavailability: what will be next?

Cabrera‐Pérez, M. Á., & Pham‐The, H. (2018) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2018.1463988 · Cited by 64 · see citing works

Aphrodisiac Performance of Bioactive Compounds from Mimosa pudica Linn.: In Silico Molecular Docking and Dynamics Simulation Approach

Palanichamy, C., Pavadai, P., Theivendren, P., Arunachalam, S., Babkiewicz, E., Pandian, S. R. K., Kabilan, S. J., Ammunje, D. N., Suthendran, K., Chandrasekaran, J., et al. (2022) · Molecules · doi:10.3390/molecules27123799 · Cited by 58 · see citing works

The Isoxazole Ring and Its N‐Oxide: A Privileged Core Structure in Neuropsychiatric Therapeutics

Pairas, G., Perperopoulou, F., Tsoungas, P. G., & Varvounis, G. (2017) · ChemMedChem · doi:10.1002/cmdc.201700023 · Cited by 57 · see citing works

Network pharmacological identification of active compounds and potential actions of Erxian decoction in alleviating menopause-related symptoms

Wang, S., Tong, Y., Ng, T., Lao, L., Lam, J. K. W., Zhang, K. Y., Zhang, Z.-J., & Sze, S. C. W. (2015) · Chinese Medicine · doi:10.1186/s13020-015-0051-z · Cited by 55 · see citing works

Lost in chemical space? Maps to support organometallic catalysis

Fey, N. (2015) · Chemistry Central Journal · doi:10.1186/s13065-015-0104-5 · Cited by 50 · see citing works

Design, Synthesis, and Molecular Docking Studies of a Conjugated Thiadiazole–Thiourea Scaffold as Antituberculosis Agents

Tatar, E., Karakuş, S., Küçükgüzel, Ş. G., Okullu, S. Ö., Ünübol, N., Kocagöz, T., Clercq, E. D., Andreï, G., Snoeck, R., Pannecouque, C. C., et al. (2016) · Biological and Pharmaceutical Bulletin · doi:10.1248/bpb.b15-00698 · Cited by 52 · see citing works

Chemical profiling of volatile compounds of the essential oil of grey-leaved rockrose (Cistus albidus L.) and its antioxidant, anti-inflammatory, antibacterial, antifungal, and anticancer activity in vitro and in silico

Elbouzidi, A., Taibi, M., Laaraj, S., Loukili, E. H., Haddou, M., Hachlafi, N. E., Mrabti, H. N., Baraich, A., Bellaouchi, R., Asehraou, A., et al. (2024) · Frontiers in Chemistry · doi:10.3389/fchem.2024.1334028 · Cited by 52 · see citing works

Synthesis, Antimicrobial, Antiquorum‐Sensing, and Cytotoxic Activities of New Series of Isoindoline‐1,3‐dione, Pyrazolo[5,1‐a]isoindole, and Pyridine Derivatives

El‐Gohary, N. S., & Shaaban, M. I. (2015) · Archiv der Pharmazie · doi:10.1002/ardp.201500037 · Cited by 47 · see citing works

Evaluation of the Interaction between Carvacrol and Thymol, Major Compounds of Ptychotis verticillata Essential Oil: Antioxidant, Anti-Inflammatory and Anticancer Activities against Breast Cancer Lines

Taibi, M., Elbouzidi, A., Haddou, M., Baraich, A., Ou-Yahia, D., Bellaouchi, R., Mothana, R. A., Al-Yousef, H. M., Asehraou, A., Addi, M., et al. (2024) · Life · doi:10.3390/life14081037 · Cited by 46 · see citing works

Quantum chemical insight into molecular structure, NBO analysis of the hydrogen-bonded interactions, spectroscopic (FT–IR, FT–Raman), drug likeness and molecular docking of the novel anti COVID-19 molecule 2-[(4,6-diaminopyrimidin-2-yl)sulfanyl]-N-(4-fluorophenyl)acetamide - dimer

Mary, S. J. J., Siddique, M. U. M., Pradhan, S., Jayaprakash, V., & James, C. (2020) · Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy · doi:10.1016/j.saa.2020.118825 · Cited by 42 · see citing works

phytochemdb: a platform for virtual screening and computer-aided drug designing

Mahmud, S., Paul, G. K., Biswas, S., Kazi, T., Mahbub, S., Mita, M. A., Afrose, S., Islam, A., Ahaduzzaman, S., Hasan, M. R., et al. (2022) · Database · doi:10.1093/database/baac002 · Cited by 42 · see citing works

Network pharmacology and molecular docking approach to elucidate the mechanisms of Liuwei Dihuang pill in diabetic osteoporosis

Lu, Z., Huang, M., Lin, H., Wang, G., & Li, H. (2022) · Journal of Orthopaedic Surgery and Research · doi:10.1186/s13018-022-03194-2 · Cited by 41 · see citing works

Impact of Applicability Domains to Generative Artificial Intelligence

Langevin, M., Grebner, C., Güssregen, S., Sauer, S., Li, Y., Matter, H., & Bianciotto, M. (2023) · ACS Omega · doi:10.1021/acsomega.3c00883 · Cited by 41 · see citing works

Synthesis, antimicrobial, antiquorum-sensing and cytotoxic activities of new series of benzothiazole derivatives

Gabr, M. T., El‐Gohary, N. S., El‐Bendary, E. R., El‐Kerdawy, M. M., Ni, N., & Shaaban, M. I. (2015) · Chinese Chemical Letters · doi:10.1016/j.cclet.2015.09.004 · Cited by 40 · see citing works

DrugLogit: Logistic Discrimination between Drugs and Nondrugs Including Disease-Specificity by Assigning Probabilities Based on Molecular Properties

García‐Sosa, A. T., Oja, M., Hetényi, C., & Maran, U. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci200587h · Cited by 38 · see citing works

A New Class of Dengue and West Nile Virus Protease Inhibitors with Submicromolar Activity in Reporter Gene DENV‑2 Protease and Viral Replication Assays

Kühl, N., Graf, D., Bock, J., Behnam, M. A. M., Leuthold, M. M., & Klein, C. D. P. (2020) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.0c00413 · Cited by 38 · see citing works

Comprehensive machine learning based study of the chemical space of herbicides

Oršolić, D., Pehar, V., Šmuc, T., & Stepanić, V. (2021) · Scientific Reports · doi:10.1038/s41598-021-90690-w · Cited by 37 · see citing works

Target-aware 3D molecular generation based on guided equivariant diffusion

Hu, Q., Sun, C., He, H., Xu, J., Liu, D., Zhang, W., Shi, S., Zhang, K., & Li, H. (2025) · Nature Communications · doi:10.1038/s41467-025-63245-0 · Cited by 35 · see citing works

Challenges with multi-objective QSAR in drug discovery

Lambrinidis, G., & Tsantili‐Kakoulidou, A. (2018) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2018.1496079 · Cited by 34 · see citing works

Synthesis, in vitro antitumor activity and molecular modeling studies of a new series of benzothiazole Schiff bases

Gabr, M. T., El‐Gohary, N. S., El‐Bendary, E. R., El‐Kerdawy, M. M., & Ni, N. (2016) · Chinese Chemical Letters · doi:10.1016/j.cclet.2015.12.033 · Cited by 34 · see citing works

Drug-likeness prediction of chemical constituents isolated from Chinese materia medica Ciwujia

Zhang, S., Li, X., & Yang, X.-Y. (2017) · Journal of Ethnopharmacology · doi:10.1016/j.jep.2017.01.002 · Cited by 34 · see citing works

Investigation into the mechanism of Eucommia ulmoides Oliv. based on a systems pharmacology approach

Li, Y., Han, C., Wang, J., Xiao, W., Wang, Z., Zhang, J., Yang, Y., Zhang, S., & Ai, C. (2013) · Journal of Ethnopharmacology · doi:10.1016/j.jep.2013.10.067 · Cited by 33 · see citing works

Thrombolytic Activity, Drug Likeness Property and ADME/T Analysis of Isolated Phytochemicals from Ginger (Zingiber officinale) Using In Silico Approaches

Hossain, S., Sarkar, B., Prottoy, M. N. I., Araf, Y., Taniya, M. A., & Ullah, M. A. (2019) · Modern Research in Inflammation · doi:10.4236/mri.2019.83003 · Cited by 33 · see citing works

Design, synthesis and biological evaluation of new series of hexahydroquinoline and fused quinoline derivatives as potent inhibitors of wild-type EGFR and mutant EGFR (L858R and T790M)

Shaheen, M. A., El-Emam, A. A.-R., & El‐Gohary, N. S. (2020) · Bioorganic Chemistry · doi:10.1016/j.bioorg.2020.104274 · Cited by 31 · see citing works

From Pyrazolones to Azaindoles: Evolution of Active-Site SHP2 Inhibitors Based on Scaffold Hopping and Bioisosteric Replacement

Mostinski, Y., Heynen, G. J. J. E., López‐Alberca, M. P., Paul, J., Miksche, S., Radetzki, S., Schaller, D., Shanina, E., Seyffarth, C., Kolomeets, Y., et al. (2020) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.0c01265 · Cited by 31 · see citing works

Antimicrobial and Antiquorum‐Sensing Studies. Part 3: Synthesis and Biological Evaluation of New Series of [1,3,4]Thiadiazoles and Fused [1,3,4]Thiadiazoles

El‐Gohary, N. S., & Shaaban, M. I. (2015) · Archiv der Pharmazie · doi:10.1002/ardp.201400381 · Cited by 30 · see citing works

Prediction of pH-Dependent Hydrophobic Profiles of Small Molecules from Miertus–Scrocco–Tomasi Continuum Solvation Calculations

Zamora, W. J., Curutchet, C., Campanera, J. M., & Luque, F. J. (2017) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.7b08311 · Cited by 29 · see citing works

Flavonol morin targets host ACE2, IMP-α, PARP-1 and viral proteins of SARS-CoV-2, SARS-CoV and MERS-CoV critical for infection and survival: a computational analysis

Gupta, A., Ahmad, R., Siddiqui, S., Yadav, K., Srivastava, A., Trivedi, A., Ahmad, B., Khan, M. A., Shrivastava, A. K., & Singh, G. K. (2021) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2021.1871863 · Cited by 29 · see citing works

Antiproliferative Activities and SwissADME Predictions of Physicochemical Properties of Carbonyl Group‐Modified Rotenone Analogues

Hernandez, R. D., Genio, F. A. F., Casanova, J., Conato, M. T., & Paderes, M. C. (2023) · ChemistryOpen · doi:10.1002/open.202300087 · Cited by 28 · see citing works

Ruellia prostrata Poir. activity evaluated by phytoconstituents, antioxidant, anti-inflammatory, antibacterial activity, and in silico molecular functions

Akhter, S., Hossain, M. W., Sultana, S., Jharna, J. F., Meghla, N. S., Alam, R., Anis‐Ul‐Haque, K. M., & Rahman, M. M. (2021) · Journal of Saudi Chemical Society · doi:10.1016/j.jscs.2021.101401 · Cited by 27 · see citing works

In silico Structure Prediction, Molecular Docking, and Dynamic Simulation of Plasmodium falciparum AP2-I Transcription Factor

Oladejo, D. O., Oduselu, G. O., Dokunmu, T. M., Isewon, I., Oyelade, J. O., Okafor, E., Iweala, E., & Adebiyi, E. F. (2023) · Bioinformatics and Biology Insights · doi:10.1177/11779322221149616 · Cited by 27 · see citing works

Density functional theory calculations, vibrational spectral analysis and topological analysis of 1-acethyl-2(4-isopropoxy-3-methoxyphenyl) cyclopropane with docking studies

Renj, D. P. L., Geetha, R. R. J., Benifa, A., Amalanathan, M., Mary, M. S. M., Ratković, Z., Muškinja, J., & Micheal, J. (2024) · Chemical Physics Impact · doi:10.1016/j.chphi.2024.100524 · Cited by 25 · see citing works

Characterization of Plant-Derived Natural Inhibitors of Dipeptidyl Peptidase-4 as Potential Antidiabetic Agents: A Computational Study

Hossain, A., Rahman, M. E., Faruqe, M. O., Saif, A., Suhi, S., Zaman, R., Hirad, A. H., Matin, M. N., Rabbee, M. F., & Baek, K.-H. (2024) · Pharmaceutics · doi:10.3390/pharmaceutics16040483 · Cited by 23 · see citing works

Molecular-Simulation-Driven Fragment Screening for the Discovery of New CXCL12 Inhibitors

Martinez-Rosell, G., Harvey, M. J., & Fabritiis, G. D. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00625 · Cited by 23 · see citing works

An Evaluation of the Potential of Essential Oils against SARS-CoV-2 from In Silico Studies through the Systematic Review Using a Chemometric Approach

Neto, L. T., Monteiro, M. L. G., Galvan, D., & Conte‐Júnior, C. A. (2021) · Pharmaceuticals · doi:10.3390/ph14111138 · Cited by 23 · see citing works

Computational Study of the Therapeutic Potential of Novel Heterocyclic Derivatives against SARS-CoV-2

Babalola, B. A., Adetobi, T. E., Akinsuyi, O., Otunba, A. A., & Folajimi, O. (2021) · COVID · doi:10.3390/covid1040061 · Cited by 22 · see citing works

Synthesis of carbohydrazides and carboxamides as anti-tubercular agents

Kumar, G., Krishna, V. S., Sriram, D., & Jachak, S. M. (2018) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2018.07.047 · Cited by 21 · see citing works

Natural polymers as potential P-glycoprotein inhibitors: Pre-ADMET profile and computational analysis as a proof of concept to fight multidrug resistance in cancer

Gandla, K., Islam, F., Zehravi, M., Karunakaran, A., Sharma, I., Haque, M. A., Kumar, S., Pratyush, K., Dhawale, S. A., Nainu, F., et al. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e19454 · Cited by 22 · see citing works

Isolation of quinic acid from dropped Citrus reticulata Blanco fruits: its derivatization, antibacterial potential, docking studies, and ADMET profiling

Heena, Kaushal, S., Kaur, V., Panwar, H., Sharma, P., & Jangra, R. (2024) · Frontiers in Chemistry · doi:10.3389/fchem.2024.1372560 · Cited by 21 · see citing works

Computational Discovery of Novel Imidazole Derivatives as Inhibitors of SARS-CoV-2 Main Protease: An Integrated Approach Combining Molecular Dynamics and Binding Affinity Analysis

Babalola, B. A., & Adegboyega, A. E. (2024) · COVID · doi:10.3390/covid4060046 · Cited by 21 · see citing works

Synthesis and biological evaluation of dihydroquinoline carboxamide derivatives as anti-tubercular agents

Kumar, G., Sathe, A., Krishna, V. S., Sriram, D., & Jachak, S. M. (2018) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2018.07.046 · Cited by 21 · see citing works

Molecular docking and network connections of active compounds from the classical herbal formula Ding Chuan Tang

Clyne, A., Yang, L., Yang, M., May, B. H., & Yang, A. W. H. (2020) · PeerJ · doi:10.7717/peerj.8685 · Cited by 21 · see citing works

Compound Properties and their Influence on Drug Quality

Chen, H., Engkvist, O., & Kogej, T. (2015) · Elsevier eBooks · doi:10.1016/b978-0-12-417205-0.00015-8 · Cited by 22 · see citing works

Indexing molecules for their hERG liability

Rayan, A., Falah, M., Raiyn, J., Da'adoosh, B., Kadan, S., Zaid, H., & Goldblum, A. (2013) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2013.04.059 · Cited by 21 · see citing works

Exploration of marine natural compounds as promising MDM2 inhibitors for treating triple-negative breast cancer: insights from molecular docking, ADME/T studies, molecular dynamics simulation and MM-PBSA binding free energy calculations

Ali, M. L., Hoque, N., Hasan, M. M., Azme, E., & Noushin, F. (2024) · Discover Chemistry. · doi:10.1007/s44371-024-00071-1 · Cited by 20 · see citing works

Screening and Toxicity Risk Assessment of Selected Compounds to Target Cancer using QSAR and Pharmacophore Modelling

Kumar, T., Kabilan, S., & Parthasarathy, V. (2017) · International Journal of PharmTech Research · doi:10.20902/ijptr.2017.10428 · Cited by 20 · see citing works

Molecular structure, NBO analysis of the hydrogen-bonded interactions, spectroscopic (FT–IR, FT–Raman), drug likeness and molecular docking of the novel anti COVID-2 molecule (2E)-N-methyl-2-[(4-oxo-4H-chromen-3-yl)methylidene]-hydrazinecarbothioamide (Dimer) - quantum chemical approach

Mary, S. J. J., Pradhan, S., & James, C. (2020) · Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy · doi:10.1016/j.saa.2020.119388 · Cited by 20 · see citing works

Exploring the Anticancer Potential of Furanpydone A: A Computational Study on its Inhibition of MTHFD2 Across Diverse Cancer Cell Lines

Alhawarri, M. B. (2024) · Cell Biochemistry and Biophysics · doi:10.1007/s12013-024-01474-8 · Cited by 19 · see citing works

In Silico Repositioning of Dopamine Modulators with Possible Application to Schizophrenia: Pharmacophore Mapping, Molecular Docking and Molecular Dynamics Analysis

Mejia-Gutierrez, M., Vásquez-Paz, B. D., Fierro, L., & Maza, J. R. (2021) · ACS Omega · doi:10.1021/acsomega.0c05984 · Cited by 18 · see citing works

The combination of molecular docking and network pharmacology reveals the molecular mechanism of Danggui Niantong decoction in treating gout

Liu, Y., Luo, D., & Xu, B. (2022) · Medicine · doi:10.1097/md.0000000000031535 · Cited by 18 · see citing works

Quinolinone-based agonists of S1P1: Use of a N-scan SAR strategy to optimize in vitro and in vivo activity

Pennington, L. D., Croghan, M. D., Sham, K. K. C., Pickrell, A. J., Harrington, P. E., Frohn, M. J., Lanman, B. A., Reed, A. B., Lee, M. R., Xu, H., et al. (2011) · Bioorganic & Medicinal Chemistry Letters · doi:10.1016/j.bmcl.2011.10.085 · Cited by 18 · see citing works

Therapeutic potential of the genus Zanthoxylum phytochemicals: A theoretical ADME/Tox analysis

Wekesa, E. N., Kimani, N. M., Kituyi, S. N., Omosa, L. K., & Santos, C. B. R. (2023) · South African Journal of Botany · doi:10.1016/j.sajb.2023.09.009 · Cited by 17 · see citing works

Synthesis, biosimulation and pharmacological evaluation of benzimidazole derivatives with antihypertensive multitarget effect

Gutiérrez-Hernández, A., Estrada-Soto, S. E., Martínez-Conde, C., Gaona-Tovar, E., Medina‐Franco, J. L., Hernández‐Núñez, E., Hidalgo‐Figueroa, S., Castro-Moreno, P., Ibarra-Barajas, M., & Navarrete‐Vázquez, G. (2024) · Bioorganic & Medicinal Chemistry Letters · doi:10.1016/j.bmcl.2024.129879 · Cited by 16 · see citing works

Computational analysis of Ayurvedic metabolites for potential treatment of drug-resistant Candida auris

Shah, M., Zia, M., Ahmad, I., Khan, M. U., Ejaz, H., Alam, M., Aziz, S., Nishan, U., Dib, H., Ullah, R., et al. (2025) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2025.1537872 · Cited by 17 · see citing works

Hybrid QSAR modeling of LD50 in organophosphorus nerve agents: a mechanistic approach using DFT and molecular docking

Jang, Y., Kim, J., Lee, D., Yoo, J. S., Park, J., & Kang, K. (2025) · Computational Toxicology · doi:10.1016/j.comtox.2025.100363 · Cited by 16 · see citing works

Design, synthesis, in silico, and in vitro evaluation of 3‐phenylpyrazole acetamide derivatives as antimycobacterial agents

Gaikwad, N. B., Nirmale, K., Sahoo, S. K., Ahmad, M. N., Kaul, G., Shukla, M., Nanduri, S., Gupta, A. D., Chopra, S., & Yaddanapudi, V. M. (2020) · Archiv der Pharmazie · doi:10.1002/ardp.202000349 · Cited by 16 · see citing works

Recent trends in the design of antimicrobial agents using Ugi-multicomponent reaction

Tandi, M., & Sundriyal, S. (2021) · Journal of the Indian Chemical Society · doi:10.1016/j.jics.2021.100106 · Cited by 16 · see citing works

How fullerene derivatives (FDs) act on therapeutically important targets associated with diabetic diseases

Fjodorova, N., Novič, M., Venko, K., Drgan, V., Rasulev, B., Saçan, M. T., Erdem, S. S., Tugcu, G., Toropova, A. P., & Toropov, A. A. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.02.006 · Cited by 16 · see citing works

Antiemetic effects of sclareol, possibly through 5-HT3 and D2 receptor interaction pathways: In-vivo and in-silico studies

Bappi, M. H., Prottay, A. A. S., Al-Khafaji, K., Akbor, M. S., Hossain, M. K., Islam, M. S., Asha, A. I., Medeiros, C. R., Tahim, C. M., Lucetti, E. C. P., et al. (2023) · Food and Chemical Toxicology · doi:10.1016/j.fct.2023.114068 · Cited by 17 · see citing works

Exploring the Interactions of Ferrocenylmethylnucleobase Derivatives With BSA and HHb: Insights From Electrochemical, Spectroscopic, ADMET, in Silico Docking, and MD Simulations

Benamor, M. L., Lanez, E., Bekkar, Y., Adaika, A., Lanez, T., Nesba, K., & Bechki, L. (2025) · ChemistrySelect · doi:10.1002/slct.202404678 · Cited by 15 · see citing works

Structure-Based Screening of Plasmodium berghei Glutathione S-Transferase Identifies CB-27 as a Novel Antiplasmodial Compound

Colón-Lorenzo, E. E., Colón-López, D. D., Vega-Rodríguez, J., Dupin, A., Fidock, D. A., Baerga‐Ortiz, A., Ortíz, J. G., Bosch, J., & Serrano, A. E. (2020) · Frontiers in Pharmacology · doi:10.3389/fphar.2020.00246 · Cited by 15 · see citing works

Many structurally related drugs bind different targets whereas distinct drugs display significant target overlap

Hu, Y., & Bajorath, J. (2012) · RSC Advances · doi:10.1039/c2ra01345b · Cited by 15 · see citing works

Investigation of salicylidene acylhydrazides derivatives: Molecular Docking, ADMET, and Molecular Dynamic Simulations were used in conjunction towards the design of new Yersinia pseudotuberculosis inhibitors

Edache, E. I., Uzairu, A., Mamza, P. A., & Shallangwa, G. A. (2022) · Turkish computational and theoretical chemistry/Turkish computational and theoretical chemistry : · doi:10.33435/tcandtc.1003157 · Cited by 14 · see citing works

Generating structural alerts from toxicology datasets using the local interpretable model-agnostic explanations method

Nascimento, C. M. C., Moura, P. G., & Pimentel, A. S. (2023) · Digital Discovery · doi:10.1039/d2dd00136e · Cited by 14 · see citing works

Selection criteria for drug‐like compounds

Muegge, I. (2003) · Medicinal Research Reviews · doi:10.1002/med.10041 · Cited by 418 · see citing works

CONCEPT OF DRUG LIKENESS IN PHARMACEUTICAL RESEARCH

Jadhav, P. B., Yadav, A. R., & Gore, M. G. (2015) · International Journal of Pharma and Bio Sciences · Cited by 32 · see citing works

Molecular properties that influence oral drug-like behavior.

Lajiness, M. S., Vieth, M., & Erickson, J. A. (2004) · PubMed · Cited by 102 · see citing works

Drug Discovery, Drug-Likeness Screening, and Bioavailability: Development of Drug-Likeness Rule for Natural Products

Ahire, E. D., Sonawane, V. N., Surana, K. R., & Talele, G. S. (2021) · Apple Academic Press eBooks · doi:10.1201/9781003054894-13 · Cited by 41 · see citing works

Design strategies for building drug-like chemical libraries.

Mitchell, T. J., & Showell, G. A. (2001) · PubMed · Cited by 21 · see citing works