Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions

Untitled work

Bartel (2004) · doi:10.1016/s0092-8674(04)00045-5 · Cited by 34,714

Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications

Sørlie, T., Perou, C. M., Tibshirani, R. J., Aas, T., Geisler, S. B., Johnsen, H., Hastie, T. J., Eisen, M. B., van de Rijn, M., Jeffrey, S. S., et al. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.191367098 · Cited by 11,506

Molecular Mechanisms of Depression: Perspectives on New Treatment Strategies

Lang, U. E., & Borgwardt, S. (2013) · Cellular Physiology and Biochemistry · doi:10.1159/000350094 · Cited by 8,526

Can the pharmaceutical industry reduce attrition rates?

Kola, I., & Landis, J. (2004) · Nature Reviews Drug Discovery · doi:10.1038/nrd1470 · Cited by 4,167

Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling

Svetnik, V., Liaw, A. I., Tong, C., Culberson, J. C., Sheridan, R. P., & Feuston, B. P. (2003) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci034160g · Cited by 3,613

MoleculeNet: a benchmark for molecular machine learning

Wu, Z., Ramsundar, B., Feinberg, E. N., Gomes, J., Geniesse, C., Pappu, A. S., Leswing, K., & Pande, V. (2018) · Chemical Science · doi:10.1039/c7sc02664a · Cited by 3,107

Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules

Gómez-Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., Aguilera-Iparraguirre, J., Hirzel, T. D., Adams, R. P., & Aspuru-Guzik, A. (2018) · ACS Central Science · doi:10.1021/acscentsci.7b00572 · Cited by 3,106

Mechanisms of Alternative Pre-Messenger RNA Splicing

Black, D. L. (2003) · Annual Review of Biochemistry · doi:10.1146/annurev.biochem.72.121801.161720 · Cited by 2,704

A Deep Learning Approach to Antibiotic Discovery

Stokes, J. M., Yang, K., Swanson, K., Jin, W., Cubillos-Ruiz, A., Donghia, N. M., MacNair, C. R., French, S., Carfrae, L. A., Bloom-Ackermann, Z., et al. (2020) · Cell · doi:10.1016/j.cell.2020.01.021 · Cited by 2,463

Schizophrenia: A Concise Overview of Incidence, Prevalence, and Mortality

McGrath, J., Saha, S., Chant, D., & Welham, J. (2008) · Epidemiologic Reviews · doi:10.1093/epirev/mxn001 · Cited by 2,280

The blood–brain barrier: an overview

Ballabh, P., Braun, A., & Nedergaard, M. (2004) · Neurobiology of Disease · doi:10.1016/j.nbd.2003.12.016 · Cited by 2,276

A Century of Alzheimer's Disease

Goedert, M., & Spillantini, M. G. (2006) · Science · doi:10.1126/science.1132814 · Cited by 2,153

A Landscape of Pharmacogenomic Interactions in Cancer

Iorio, F., Knijnenburg, T. A., Vis, D. J., Bignell, G. R., Menden, M. P., Schubert, M., Aben, N., Gonçalves, E., Barthorpe, S., Lightfoot, H., et al. (2016) · Cell · doi:10.1016/j.cell.2016.06.017 · Cited by 2,090

Neurobehavioural effects of developmental toxicity

Grandjean, P., & Landrigan, P. J. (2014) · The Lancet Neurology · doi:10.1016/s1474-4422(13)70278-3 · Cited by 1,886

Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules.

Gómez‐Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., Aguilera‐Iparraguirre, J., Hirzel, T. D., Adams, R. P., & Aspuru‐Guzik, A. (2018) · Cambridge University Engineering Department Publications Database · doi:10.17863/cam.34134 · Cited by 1,813

Alzheimer’s disease drug-development pipeline: Few candidates, frequent failures

Cummings, J. L., Morstorf, T., & Zhong, K. (2014) · Alzheimer's Research & Therapy · doi:10.1186/alzrt269 · Cited by 1,744

Deep Reinforcement Learning That Matters

Henderson, P., Islam, R., Bachman, P., Pineau, J., Precup, D., & Meger, D. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11694 · Cited by 1,685

Deep learning enables rapid identification of potent DDR1 kinase inhibitors

Zhavoronkov, A., Ivanenkov, Y. A., Aliper, A., Veselov, M. S., Aladinskiy, V. A., Aladinskaya, A. V., Terentiev, V. A., Polykovskiy, D. A., Kuznetsov, M. D., Asadulaev, A., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0224-x · Cited by 1,573

Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks

Segler, M. H. S., Kogej, T., Tyrchan, C., & Waller, M. P. (2018) · ACS Central Science · doi:10.1021/acscentsci.7b00512 · Cited by 1,537

Multi-Omics Factor Analysis—a framework for unsupervised integration of multi-omics data sets

Argelaguet, R., Velten, B., Arnol, D., Dietrich, S., Zenz, T., Marioni, J. C., Buettner, F., Huber, W., & Stegle, O. (2018) · Molecular Systems Biology · doi:10.15252/msb.20178124 · Cited by 1,510

Modeling polypharmacy side effects with graph convolutional networks

Zitnik, M., Agrawal, M., & Leskovec, J. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty294 · Cited by 1,501

Molecular de-novo design through deep reinforcement learning

Olivecrona, M., Blaschke, T., Engkvist, O., & Chen, H. (2017) · Journal of Cheminformatics · doi:10.1186/s13321-017-0235-x · Cited by 1,443

Deep reinforcement learning for de novo drug design

Popova, M., Isayev, O., & Tropsha, A. (2018) · Science Advances · doi:10.1126/sciadv.aap7885 · Cited by 1,433

Improved protein structure prediction using predicted interresidue orientations

Yang, J., Anishchenko, I., Park, H., Peng, Z., Ovchinnikov, S., & Baker, D. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1914677117 · Cited by 1,421

Transforming Auto-Encoders

Hinton, G. E., Krizhevsky, A., & Wang, S. D. (2011) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-21735-7_6 · Cited by 1,360

Concepts and applications of molecular similarity

Johnson, M. A., & Maggiora, G. M. (1990) · Wiley eBooks · Cited by 1,301

Structure-Based Virtual Screening for Drug Discovery: Principles, Applications and Recent Advances

Lionta, E., Spyrou, G. M., Vassilatis, D. K., & Cournia, Z. (2014) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026614666140929124445 · Cited by 1,256

Toxic Proteins in Neurodegenerative Disease

Taylor, J. P., Hardy, J., & Fischbeck, K. H. (2002) · Science · doi:10.1126/science.1067122 · Cited by 1,251

The human splicing code reveals new insights into the genetic determinants of disease

Xiong, H. Y., Alipanahi, B., Lee, L. J., Bretschneider, H., Merico, D., Yuen, R. K. C., Hua, Y., Gueroussov, S., Najafabadi, H. S., Hughes, T., et al. (2014) · Science · doi:10.1126/science.1254806 · Cited by 1,243

Alzheimer’s disease: Clinical trials and drug development

Mangialasche, F., Solomon, A., Winblad, B., Mecocci, P., & Kivipelto, M. (2010) · The Lancet Neurology · doi:10.1016/s1474-4422(10)70119-8 · Cited by 1,217

Proteomic Analysis of the Cerebrospinal Fluid of Parkinson's Disease Patients Pre- and Post-Deep Brain Stimulation

Wang, E., Yao, H.-B., Chen, Y., Wang, G., Gao, W., Sun, Y., Guo, J., Hu, J., Jiang, C., & Hu, J. (2013) · Cellular Physiology and Biochemistry · doi:10.1159/000350082 · Cited by 1,185

Deep Neural Nets as a Method for Quantitative Structure–Activity Relationships

Ma, J., Sheridan, R. P., Liaw, A., Dahl, G. E., & Svetnik, V. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/ci500747n · Cited by 1,159

Prediction of drug–target interaction networks from the integration of chemical and genomic spaces

Yamanishi, Y., Araki, M., Gutteridge, A., Honda, W., & Kanehisa, M. I. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn162 · Cited by 1,148

DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays

Singh, A., Shannon, C. P., Gautier, B., Rohart, F., Vacher, M. A., Tebbutt, S. J., & Cao, K. L. (2019) · Bioinformatics · doi:10.1093/bioinformatics/bty1054 · Cited by 1,124

DeepTox: toxicity prediction using deep learning

Mayr, A., Klambauer, G., Unterthiner, T., & Hochreiter, S. (2016) · Frontiers in Environmental Science · doi:10.3389/fenvs.2015.00080 · Cited by 1,081

Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery

Yang, X. Y., Wang, Y., Byrne, R., Schneider, G., & Yang, S. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00728 · Cited by 1,079

Artificial intelligence in drug development: Present status and future prospects

Mak, K.-K., & Pichika, M. R. (2019) · Drug Discovery Today · doi:10.1016/j.drudis.2018.11.014 · Cited by 1,018

A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking

Ballester, P. J., & Mitchell, J. B. O. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq112 · Cited by 1,015

Ageing and Parkinson's disease: Why is advancing age the biggest risk factor?

Reeve, A. K., Simcox, E. M., & Turnbull, D. M. (2014) · Ageing Research Reviews · doi:10.1016/j.arr.2014.01.004 · Cited by 1,013

Artificial intelligence for mental health and mental illnesses: an overview

Graham, S., Depp, C., Lee, E. E., Nebeker, C., Tu, X., Kim, H.-C., & Jeste, D. V. (2019) · Current Psychiatry Reports · doi:10.1007/s11920-019-1094-0 · Cited by 970

Machine Learning Approaches for Clinical Psychology and Psychiatry

Dwyer, D. B., Falkai, P., & Koutsouleris, N. (2018) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-032816-045037 · Cited by 966

Opportunities and challenges in phenotypic drug discovery: an industry perspective

Moffat, J. G., Vincent, F., Lee, J. A., Eder, J., & Prunotto, M. (2017) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2017.111 · Cited by 939

Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model

Wang, S., Sun, S., Li, Z., Zhang, R., & Xu, J. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005324 · Cited by 931

PREDICT: a method for inferring novel drug indications with application to personalized medicine

Gottlieb, A., Stein, G. Y., Ruppin, E., & Sharan, R. (2011) · Molecular Systems Biology · doi:10.1038/msb.2011.26 · Cited by 900

Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines

Geeleher, P., Cox, N. J., & Huang, R. S. (2014) · Genome biology · doi:10.1186/gb-2014-15-3-r47 · Cited by 897

Computation of Octanol−Water Partition Coefficients by Guiding an Additive Model with Knowledge

Cheng, T., Zhao, Y., Li, X., Fu, L., Xu, Y., Zhang, X., Li, Y., Wang, R., & Lai, L. (2007) · Journal of Chemical Information and Modeling · doi:10.1021/ci700257y · Cited by 897

Machine Learning for Precision Psychiatry: Opportunities and Challenges

Bzdok, D., & Meyer‐Lindenberg, A. (2017) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2017.11.007 · Cited by 888

Molecular fingerprint similarity search in virtual screening

Cereto‐Massagué, A., Ojeda, M. J., Valls, C., Mulero, M., Garcı́a-Vallvé, S., & Pujadas, G. (2014) · Methods · doi:10.1016/j.ymeth.2014.08.005 · Cited by 881

Protein–Ligand Scoring with Convolutional Neural Networks

Ragoza, M., Hochuli, J. E., Idrobo, E., Sunseri, J., & Koes, D. R. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00740 · Cited by 853

Similarity-based virtual screening using 2D fingerprints

Willett, P. (2006) · Drug Discovery Today · doi:10.1016/j.drudis.2006.10.005 · Cited by 849

Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis

Shen, R., Olshen, A. B., & Ladanyi, M. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp543 · Cited by 849

Recent Advancements in Pathogenesis, Diagnostics and Treatment of Alzheimer’s Disease

Khan, S., Barve, K. H., & Kumar, M. S. (2020) · Current Neuropharmacology · doi:10.2174/1570159x18666200528142429 · Cited by 820 · cited 1×

Functional ultrasound imaging of the brain

Macé, É., Montaldo, G., Cohen, I., Baulac, M., Fink, M., & Tanter, M. (2011) · Nature Methods · doi:10.1038/nmeth.1641 · Cited by 806

Drug discovery and development: Role of basic biological research

Mohs, R. C., & Greig, N. H. (2017) · Alzheimer s & Dementia Translational Research & Clinical Interventions · doi:10.1016/j.trci.2017.10.005 · Cited by 708

Deep learning improves prediction of drug–drug and drug–food interactions

Ryu, J. Y., Kim, H. U., & Lee, S. Y. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1803294115 · Cited by 688

Graph Convolutional Policy Network for Goal-Directed Molecular Graph GenerationPreprint

You, J., Liu, B., Ying, R., Pande, V. S., & Leskovec, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.02473 · Cited by 688

Cross-trial prediction of treatment outcome in depression: a machine learning approach

Chekroud, A. M., Zotti, R. J., Shehzad, Z., Gueorguieva, R., Johnson, M. K., Trivedi, M. H., Cannon, T. D., Krystal, J. H., & Corlett, P. R. (2016) · The Lancet Psychiatry · doi:10.1016/s2215-0366(15)00471-x · Cited by 676

Development and evaluation of a deep learning model for protein–ligand binding affinity prediction

Stepniewska-Dziubinska, M. M., Zielenkiewicz, P., & Siedlecki, P. S. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty374 · Cited by 673

DeepSynergy: predicting anti-cancer drug synergy with Deep Learning

Preuer, K., Lewis, R. P. I., Hochreiter, S., Bender, A., Bulusu, K. C., & Klambauer, G. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx806 · Cited by 668

Advancing drug discovery via artificial intelligence

Chan, H. C. S., Shan, H., Dahoun, T., Vogel, H., & Yuan, S. (2019) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2019.06.004 · Cited by 654

Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data

Aliper, A., Plis, S., Artemov, A., Ulloa, A., Mamoshina, P., & Zhavoronkov, A. (2016) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.6b00248 · Cited by 610

druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico

Kadurin, A., Nikolenko, S., Khrabrov, K., Aliper, A., & Zhavoronkov, A. (2017) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.7b00346 · Cited by 587

Deep Architectures and Deep Learning in Chemoinformatics: The Prediction of Aqueous Solubility for Drug-Like Molecules

Lusci, A., Pollastri, G., & Baldi, P. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci400187y · Cited by 575

Joint and individual variation explained (JIVE) for integrated analysis of multiple data types

Lock, E. F., Hoadley, K. A., Marron, J. S., & Nobel, A. B. (2013) · The Annals of Applied Statistics · doi:10.1214/12-aoas597 · Cited by 572

Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis

Shen, R., Olshen, A. B., & Ladanyi, M. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp659 · Cited by 564

deepDR: a network-based deep learning approach toin silicodrug repositioning

Zeng, X., Zhu, S., Liu, X., Zhou, Y., Nussinov, R., & Cheng, F. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz418 · Cited by 540

Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models

Liu, B., Ramsundar, B., Kawthekar, P., Shi, J., Gomes, J., Luu Nguyen, Q., Ho, S., Sloane, J., Wender, P., & Pande, V. (2017) · ACS Central Science · doi:10.1021/acscentsci.7b00303 · Cited by 536

Generative Recurrent Networks for De Novo Drug Design

Gupta, A., Müller, A. T., Huisman, B. J. H., Fuchs, J. A., Schneider, P., & Schneider, G. (2017) · Molecular Informatics · doi:10.1002/minf.201700111 · Cited by 494

Convolutional embedding of attributed molecular graphs for physical property prediction

Coley, C. W., Barzilay, R., Green, W. H., Jaakkola, T. S., & Jensen, K. F. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00601 · Cited by 470

Deep learning of the tissue-regulated splicing code

Leung, M. K. K., Xiong, H. Y., Lee, L. J., & Frey, B. J. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu277 · Cited by 459

MolGAN: An implicit generative model for small molecular graphs

De Cao, N., & Kipf, T. (2018) · arXiv · doi:10.48550/arxiv.1805.11973 · Cited by 442

De novo generation of hit-like molecules from gene expression signatures using artificial intelligence

Méndez‐Lucio, O., Baillif, B., Clevert, D.-A., Rouquié, D., & Wichard, J. (2020) · Nature Communications · doi:10.1038/s41467-019-13807-w · Cited by 441

Molecular generative model based on conditional variational autoencoder for de novo molecular design

Lim, J., Ryu, S., Kim, J. W., & Kim, W. Y. (2018) · Journal of Cheminformatics · doi:10.1186/s13321-018-0286-7 · Cited by 421

MOLI: multi-omics late integration with deep neural networks for drug response prediction

Sharifi-Noghabi, H., Zolotareva, O., Collins, C. C., & Ester, M. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz318 · Cited by 421

ChemDes: an integrated web-based platform for molecular descriptor and fingerprint computation

Dong, J. N., Cao, D., Miao, H., Shao, L., Deng, B., Yun, Y., Wang, N., Lü, A., Zeng, W., & Chen, A. F. (2015) · Journal of Cheminformatics · doi:10.1186/s13321-015-0109-z · Cited by 415

Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces

Xia, Z., Wu, L., Zhou, X., & Wong, S. T. C. (2010) · BMC Systems Biology · doi:10.1186/1752-0509-4-s2-s6 · Cited by 397

Boosting docking-based virtual screening with deep learning.

Pereira, J. C., Caffarena, E. R., & dos Santos, C. N. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00355 · Cited by 395

Reinforced Adversarial Neural Computer for de Novo Molecular Design

Lane, E., Asadulaev, A., Ivanenkov, Y. A., Aladinskiy, V., Sánchez-Lengeling, B., Aspuru‐Guzik, A., & Zhavoronkov, A. A. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00690 · Cited by 388

Detection of biomarkers with a multiplex quantitative proteomic platform in cerebrospinal fluid of patients with neurodegenerative disorders

Abdi, F. A., Quinn, J. F., Jankovic, J. J., McIntosh, M. W., Leverenz, J. B., Peskind, E. R., Nixon, R., Nutt, J. G., Chung, K., Zabetian, C. P., et al. (2006) · Journal of Alzheimer s Disease · doi:10.3233/jad-2006-9309 · Cited by 383

Prediction ofn-Octanol/Water Partition Coefficients from PHYSPROP Database Using Artificial Neural Networks and E-State Indices

Tetko, I. V., Tanchuk, V. Y., & Villa, A. E. P. (2001) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci010368v · Cited by 382

NeoDTI: neural integration of neighbor information from a heterogeneous network for discovering new drug–target interactions

Wan, F., Hong, L., Xiao, A., Jiang, T., & Zeng, J. (2019) · Bioinformatics · doi:10.1093/bioinformatics/bty543 · Cited by 381

Constrained Graph Variational Autoencoders for Molecule DesignPreprint

Liu, Q., Allamanis, M., Brockschmidt, M., & Gaunt, A. L. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.09076 · Cited by 375

Use of Artificial Intelligence in the Design of Small Peptide Antibiotics Effective against a Broad Spectrum of Highly Antibiotic-Resistant Superbugs

Cherkasov, A. R., Hilpert, K., Jenssen, H., Fjell, C. D., Waldbrook, M., Mullaly, S. C., Volkmer, R., & Hancock, R. E. W. (2008) · ACS Chemical Biology · doi:10.1021/cb800240j · Cited by 367

Regularized Generalized Canonical Correlation Analysis

Tenenhaus, A., & Tenenhaus, M. (2011) · Psychometrika · doi:10.1007/s11336-011-9206-8 · Cited by 361

Machine‐learning scoring functions to improve structure‐based binding affinity prediction and virtual screening

Ain, Q. U., Aleksandrova, A., Roessler, F. D., & Ballester, P. J. (2015) · WIREs Computational Molecular Science · doi:10.1002/wcms.1225 · Cited by 357

Estimation of ADME Properties with Substructure Pattern Recognition

Shen, J., Cheng, F., Xu, Y., Li, W., & Tang, Y. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci100104j · Cited by 356

Classification of Kinase Inhibitors Using a Bayesian Model

Xia, X., Maliski, E. G., Gallant, P., & Rogers, D. O. W. (2004) · Journal of Medicinal Chemistry · doi:10.1021/jm0303195 · Cited by 352

The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology

Kadurin, A., Aliper, A., Kazennov, A., Mamoshina, P., Vanhaelen, Q., Khrabrov, K., & Zhavoronkov, A. (2017) · Oncotarget · doi:10.18632/oncotarget.14073 · Cited by 350

Drug repositioning: a machine-learning approach through data integration

Napolitano, F., Zhao, Y., Moreira, V. M., Tagliaferri, R., Kere, J., D’Amato, M., & Greco, D. (2013) · Journal of Cheminformatics · doi:10.1186/1758-2946-5-30 · Cited by 349

Recent Advances in Ligand-Based Drug Design: Relevance and Utility of the Conformationally Sampled Pharmacophore Approach

Acharya, C., Coop, A., Polli, J. E., & MacKerell, A. D. (2010) · Current Computer - Aided Drug Design · doi:10.2174/157340911793743547 · Cited by 343

Target discovery

Lindsay, M. A. (2003) · Nature Reviews Drug Discovery · doi:10.1038/nrd1202 · Cited by 343

Prediction of Biological Targets for Compounds Using Multiple-Category Bayesian Models Trained on Chemogenomics Databases

Nidhi, N., Glick, M., Davies, J. W., & Jenkins, J. L. (2006) · Journal of Chemical Information and Modeling · doi:10.1021/ci060003g · Cited by 340

A Bayesian Approach to in Silico Blood-Brain Barrier Penetration Modeling

Martins, I. F. D. S., Teixeira, A. L., Pinheiro, L., & Falcão, A. O. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci300124c · Cited by 336

Current Trends in Ligand-Based Virtual Screening: Molecular Representations, Data Mining Methods, New Application Areas, and Performance Evaluation

Geppert, H., Vogt, M., & Bajorath, J. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci900419k · Cited by 335

AlphaFold at CASP13

AlQuraishi, M. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz422 · Cited by 333

TopologyNet: Topology based deep convolutional and multi-task neural networks for biomolecular property predictions

Cang, Z., & Wei, G.-W. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005690 · Cited by 330

Properties and identification of human protein drug targets

Bakheet, T. M., & Doig, A. J. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp002 · Cited by 329

Prediction of microRNAs Associated with Human Diseases Based on Weighted k Most Similar Neighbors

Xuan, P., Han, K., Guo, M., Guo, Y., Li, J., Ding, J., Liu, Y., Dai, Q., Li, J., Teng, Z., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0070204 · Cited by 324

The $2.6 Billion Pill — Methodologic and Policy Considerations

Avorn, J. (2015) · New England Journal of Medicine · doi:10.1056/nejmp1500848 · Cited by 322

Identification of Novel Antibacterial Peptides by Chemoinformatics and Machine Learning

Fjell, C. D., Jenssen, H., Hilpert, K., Cheung, W., Panté, N., Hancock, R. E. W., & Cherkasov, A. R. (2009) · Journal of Medicinal Chemistry · doi:10.1021/jm8015365 · Cited by 302

An electroencephalographic signature predicts antidepressant response in major depression

Wu, W., Zhang, Y., Jiang, J., Lucas, M. V., Fonzo, G. A., Rolle, C. E., Cooper, C., Chin-Fatt, C., Krepel, N., Cornelssen, C. A., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-019-0397-3 · Cited by 302

An Introduction to Terminology and Methodology of Chemical Synergy—Perspectives from Across Disciplines

Roell, K. R., Reif, D. M., & Motsinger‐Reif, A. A. (2017) · Frontiers in Pharmacology · doi:10.3389/fphar.2017.00158 · Cited by 299

Deep learning methods in protein structure prediction

Torrisi, M., Pollastri, G., & Le, Q. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2019.12.011 · Cited by 297

Aggression in autism spectrum disorder: presentation and treatment options

Fitzpatrick, S. E., Srivorakiat, L., Wink, L. K., Pedapati, E. V., & Erickson, C. A. (2016) · Neuropsychiatric Disease and Treatment · doi:10.2147/ndt.s84585 · Cited by 296

On the nature of cavities on protein surfaces: Application to the identification of drug‐binding sites

Nayal, M., & Honig, B. H. (2006) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.20897 · Cited by 292

Drug—drug interaction through molecular structure similarity analysis

Vilar, S., Harpaz, R., Uriarte, E., Santana, L., Rabadán, R., & Friedman, C. (2012) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2012-000935 · Cited by 290

An overview of molecular fingerprint similarity search in virtual screening

Muegge, I., & Mukherjee, P. K. (2015) · Expert Opinion on Drug Discovery · doi:10.1517/17460441.2016.1117070 · Cited by 290

In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts

Yang, H., Sun, L., Li, W., Liu, G., & Tang, Y. (2018) · Frontiers in Chemistry · doi:10.3389/fchem.2018.00030 · Cited by 287

A Bayesian machine learning approach for drug target identification using diverse data types

Madhukar, N. S., Khade, P. K., Huang, L., Gayvert, K. M., Galletti, G., Stogniew, M., Allen, J. E., Giannakakou, P., & Elemento, O. (2019) · Nature Communications · doi:10.1038/s41467-019-12928-6 · Cited by 285

Entangled Conditional Adversarial Autoencoder for de Novo Drug Discovery

Polykovskiy, D., Zhebrak, A., Vetrov, D., Ivanenkov, Y., Aladinskiy, V., Mamoshina, P., Bozdaganyan, M., Aliper, A., Zhavoronkov, A., & Kadurin, A. (2018) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.8b00839 · Cited by 278

Reevaluating the Efficacy and Predictability of Antidepressant Treatments: A Symptom Clustering Approach

Chekroud, A. M., Gueorguieva, R., Krumholz, H. M., Trivedi, M. H., Krystal, J. H., & McCarthy, G. (2017) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2017.0025 · Cited by 276

Mol-CycleGAN: a generative model for molecular optimization

Maziarka, Ł., Pocha, A., Kaczmarczyk, J., Rataj, K., Danel, T., & Warchoł, M. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-019-0404-1 · Cited by 275

Extraction of relations between genes and diseases from text and large-scale data analysis: implications for translational research

Bravo, Á., Piñero, J., Queralt-Rosiñach, N., Rautschka, M., & Furlong, L. I. (2015) · BMC Bioinformatics · doi:10.1186/s12859-015-0472-9 · Cited by 266

Quo Vadis, Virtual Screening? A Comprehensive Survey of Prospective Applications

Ripphausen, P., Nisius, B., Peltason, L., & Bajorath, J. (2010) · Journal of Medicinal Chemistry · doi:10.1021/jm101020z · Cited by 263

Assessing the impact of generative AI on medicinal chemistry

Walters, W. P., & Murcko, M. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0418-2 · Cited by 262

Connecting Peptide Physicochemical and Antimicrobial Properties by a Rational Prediction Model

Torrent, M., Andreu, D., Nogués, M. V., & Boix, E. (2011) · PLoS ONE · doi:10.1371/journal.pone.0016968 · Cited by 261

NNScore: A Neural-Network-Based Scoring Function for the Characterization of Protein−Ligand Complexes

Durrant, J. D., & McCammon, J. A. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci100244v · Cited by 261

Revolution Stalled

Hyman, S. E. (2012) · Science Translational Medicine · doi:10.1126/scitranslmed.3003142 · Cited by 260

Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis

Struble, T. J., Alvarez, J. C., Brown, S. P., Chytil, M., Cisar, J. S., DesJarlais, R. L., Engkvist, O., Frank, S. A., Greve, D. R., Griffin, D. J., et al. (2020) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.9b02120 · Cited by 254

Structure-based virtual screening of chemical libraries for drug discovery

Ghosh, S., Nie, A., An, J., & Huang, Z. (2006) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2006.04.002 · Cited by 253

Improving AutoDock Vina using random forest: the growing accuracy of binding affinity prediction by the effective exploitation of larger data sets

Li, H., Leung, K., Wong, M., & Ballester, P. J. (2015) · Molecular Informatics · doi:10.1002/minf.201400132 · Cited by 252

Tox21Challenge to Build Predictive Models of Nuclear Receptor and Stress Response Pathways as Mediated by Exposure to Environmental Chemicals and Drugs

Huang, R., Xia, M., Nguyễn, Ð.-T., Zhao, T., Sakamuru, S., Zhao, J., Shahane, S. A., Rossoshek, A., & Simeonov, A. M. (2016) · Frontiers in Environmental Science · doi:10.3389/fenvs.2015.00085 · Cited by 250

Comprehensive ensemble in QSAR prediction for drug discovery

Kwon, S., Bae, H., Jo, J., & Yoon, S. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-3135-4 · Cited by 250

Regulation of ABC efflux transporters at blood-brain barrier in health and neurological disorders

Qosa, H., Miller, D. S., Pasinelli, P., & Trotti, D. (2015) · Brain Research · doi:10.1016/j.brainres.2015.07.005 · Cited by 249

What makes a good drug target?

Gashaw, I., Ellinghaus, P., Sommer, A., & Asadullah, K. (2011) · Drug Discovery Today · doi:10.1016/j.drudis.2011.09.007 · Cited by 248

Molecular similarity and diversity in chemoinformatics: From theory to applications

Maldonado, A. G., Doucet, J. P., Petitjean, M., & Fan, B. (2006) · Molecular Diversity · doi:10.1007/s11030-006-8697-1 · Cited by 242

In silicoADME-Tox modeling: progress and prospects

Alqahtani, S. (2017) · Expert Opinion on Drug Metabolism & Toxicology · doi:10.1080/17425255.2017.1389897 · Cited by 242

Demystifying Multitask Deep Neural Networks for Quantitative Structure–Activity Relationships

Xu, Y., Ma, J., Liaw, A., Sheridan, R. P., & Svetnik, V. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00087 · Cited by 238

A multiple kernel learning algorithm for drug-target interaction prediction

do Nascimento, A. C. A., Prudêncio, R. B. C., & Costa, I. G. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-0890-3 · Cited by 235

A Machine Learning-Based Method To Improve Docking Scoring Functions and Its Application to Drug Repurposing

Kinnings, S., Liu, N., Tonge, P. J., Jackson, R. M., Xie, L., & Bourne, P. E. (2011) · Journal of Chemical Information and Modeling · doi:10.1021/ci100369f · Cited by 232

Learning Molecular Representations for Medicinal Chemistry

Chuang, K. V., Gunsalus, L. M., & Keiser, M. J. (2020) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.0c00385 · Cited by 232

DeepSMILES: An Adaptation of SMILES for Use in Machine-Learning of Chemical StructuresPreprint

O’Boyle, N. M., & Dalke, A. (2018) · ChemRxiv · doi:10.26434/chemrxiv.7097960.v1 · Cited by 230

Predictive Multitask Deep Neural Network Models for ADME-Tox Properties: Learning from Large Data Sets

Wenzel, J., Matter, H., & Schmidt, F. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00785 · Cited by 230

Prioritizing Candidate Disease miRNAs by Topological Features in the miRNA Target–Dysregulated Network: Case Study of Prostate Cancer

Xu, J., Li, C., Lv, J.-Y., Li, Y.-S., Xiao, Y., Shao, T., Huo, X., Li, X., Zou, Y., Han, Q., et al. (2011) · Molecular Cancer Therapeutics · doi:10.1158/1535-7163.mct-11-0055 · Cited by 229

Shape-Based Generative Modeling for de Novo Drug Design

Skalic, M., Jiménez, J., Sabbadin, D., & De Fabritiis, G. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00706 · Cited by 229

Identifying the macromolecular targets of de novo-designed chemical entities through self-organizing map consensus

Reker, D., Rodrigues, T., Schneider, P., & Schneider, G. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1320001111 · Cited by 228

DeepCC: a novel deep learning-based framework for cancer molecular subtype classification

Gao, F., Wang, W., Tan, M., Zhu, L., Zhang, Y., Fessler, E., Vermeulen, L., & Wang, X. (2019) · Oncogenesis · doi:10.1038/s41389-019-0157-8 · Cited by 226

Detecting Neuroimaging Biomarkers for Schizophrenia: A Meta-Analysis of Multivariate Pattern Recognition Studies

Kambeitz, J. P., Kambeitz‐Ilankovic, L., Leucht, S., Wood, S. J., Davatzikos, C. A., Malchow, B., Falkai, P., & Koutsouleris, N. (2015) · Neuropsychopharmacology · doi:10.1038/npp.2015.22 · Cited by 221

Toward the Autism Motor Signature: Gesture patterns during smart tablet gameplay identify children with autism

Anzulewicz, A., Sobota, K., & Delafield‐Butt, J. T. (2016) · Scientific Reports · doi:10.1038/srep31107 · Cited by 220

Combining Drug and Gene Similarity Measures for Drug-Target Elucidation

Perlman, L., Gottlieb, A., Atias, N., Ruppin, E., & Sharan, R. (2011) · Journal of Computational Biology · doi:10.1089/cmb.2010.0213 · Cited by 218

Multisite prediction of 4-week and 52-week treatment outcomes in patients with first-episode psychosis: a machine learning approach

Koutsouleris, N., Kahn, R. S., Chekroud, A. M., Leucht, S., Falkai, P., Wobrock, T., Derks, E. M., Fleischhacker, W., & Hasan, A. (2016) · The Lancet Psychiatry · doi:10.1016/s2215-0366(16)30171-7 · Cited by 217

Structure-based ligand discovery for the Large-neutral Amino Acid Transporter 1, LAT-1

Geier, E. G., Schlessinger, A., Fan, H., Gable, J. E., Irwin, J. J., Šali, A., & Giacomini, K. M. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1218165110 · Cited by 215

Bayer’s in silico ADMET platform: a journey of machine learning over the past two decades

Göller, A. H., Kuhnke, L., Montanari, F., Bonin, A., Schneckener, S., ter Laak, A., Wichard, J., Lobell, M., & Hillisch, A. (2020) · Drug Discovery Today · doi:10.1016/j.drudis.2020.07.001 · Cited by 215

Gaussian Processes: A Method for Automatic QSAR Modeling of ADME Properties

Obrezanova, O., Cśanyi, G., Gola, J. M. R., & Segall, M. (2007) · Journal of Chemical Information and Modeling · doi:10.1021/ci7000633 · Cited by 214

Putting the Patient Back Together — Social Medicine, Network Medicine, and the Limits of Reductionism

Greene, J. A., & Loscalzo, J. (2017) · New England Journal of Medicine · doi:10.1056/nejmms1706744 · Cited by 213

Machine learning models for drug–target interactions: current knowledge and future directions

D’Souza, S., Prema, K. V., & Balaji, S. (2020) · Drug Discovery Today · doi:10.1016/j.drudis.2020.03.003 · Cited by 213

Improvement in ADMET Prediction with Multitask Deep Featurization

Feinberg, E. N., Joshi, E., Pande, V. S., & Cheng, A. C. (2020) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.9b02187 · Cited by 210

In Silico Prediction of Blood–Brain Barrier Permeability of Compounds by Machine Learning and Resampling Methods

Wang, Z., Yang, H., Wu, Z., Wang, T., Li, W., Tang, Y., & Liu, G. (2018) · ChemMedChem · doi:10.1002/cmdc.201800533 · Cited by 209

Deep‐learning contact‐map guided protein structure prediction in CASP13

Zheng, W., Li, Y., Zhang, C., Pearce, R., Mortuza, S. M., & Zhang, Y. (2019) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.25792 · Cited by 208

Drug combination sensitivity scoring facilitates the discovery of synergistic and efficacious drug combinations in cancer

Malyutina, A., Majumder, M. M., Wang, W., Pessia, A., Heckman, C. A., & Tang, J. P. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006752 · Cited by 207

Machine Learning in Virtual Screening

Melville, J. L., Burke, E. K., & Hirst, J. D. (2009) · Combinatorial Chemistry & High Throughput Screening · doi:10.2174/138620709788167980 · Cited by 203

Deep learning-based multi-omics data integration reveals two prognostic subtypes in high-risk neuroblastoma

Zhang, L., Lv, C., Jin, Y., Cheng, G., Fu, Y., Yuan, D., Tao, Y., Guo, Y., Ni, X., & Shi, T. (2018) · Frontiers in Genetics · doi:10.3389/fgene.2018.00477 · Cited by 203

QSAR Modeling of the Blood–Brain Barrier Permeability for Diverse Organic Compounds

Zhang, L., Zhu, H., Oprea, T. I., Golbraikh, A., & Tropsha, A. (2008) · Pharmaceutical Research · doi:10.1007/s11095-008-9609-0 · Cited by 201

In Silico Prediction of Physicochemical Properties of Environmental Chemicals Using Molecular Fingerprints and Machine Learning

Zang, Q., Mansouri, K., Williams, A. J., Judson, R., Allen, D., Casey, W., & Kleinstreuer, N. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00625 · Cited by 199

Precision Oncology beyond Targeted Therapy: Combining Omics Data with Machine Learning Matches the Majority of Cancer Cells to Effective Therapeutics

Ding, M. Q., Chen, L., Cooper, G. F., Young, J. D., & Lu, X. (2017) · Molecular Cancer Research · doi:10.1158/1541-7786.mcr-17-0378 · Cited by 195

Optimizing distributions over molecular space. An Objective-Reinforced Generative Adversarial Network for Inverse-design Chemistry (ORGANIC)Preprint

Sánchez-Lengeling, B., Outeiral, C., Guimaraes, G. L., & Aspuru‐Guzik, A. (2017) · ChemRxiv · doi:10.26434/chemrxiv.5309668.v3 · Cited by 195

An in silico platform for predicting, screening and designing of antihypertensive peptides

Kumar, R., Chaudhary, K., Chauhan, J. S., Nagpal, G., Kumar, R., Sharma, M., & Raghava, G. P. S. (2015) · Scientific Reports · doi:10.1038/srep12512 · Cited by 193

ID-Score: A New Empirical Scoring Function Based on a Comprehensive Set of Descriptors Related to Protein–Ligand Interactions

Li, G., Yang, L., Wang, W., Li, L., & Yang, S. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci300493w · Cited by 181

Regularized Generalized Canonical Correlation Analysis: A Framework for Sequential Multiblock Component Methods

Tenenhaus, M., Tenenhaus, A., & Groenen, P. J. F. (2017) · Psychometrika · doi:10.1007/s11336-017-9573-x · Cited by 177

SFCscore RF : A Random Forest-Based Scoring Function for Improved Affinity Prediction of Protein–Ligand Complexes

Zilian, D., & Sotriffer, C. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci400120b · Cited by 176

Applying machine learning techniques for ADME-Tox prediction: a review

Maltarollo, V. G., Gertrudes, J. C., Oliveira, P. R., & Honorio, K. M. (2014) · Expert Opinion on Drug Metabolism & Toxicology · doi:10.1517/17425255.2015.980814 · Cited by 176

A systematic approach to identify novel cancer drug targets using machine learning, inhibitor design and high-throughput screening

Jeon, J., Nim, S., Teyra, J., Datti, A., Wrana, J. L., Sidhu, S. S., Moffat, J., & Kim, P. M. (2014) · Genome Medicine · doi:10.1186/s13073-014-0057-7 · Cited by 174

Dr.VAE: improving drug response prediction via modeling of drug perturbation effects

Rampášek, L., Hidru, D., Smirnov, P., Haibe‐Kains, B., & Goldenberg, A. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz158 · Cited by 174

Activity, assay and target data curation and quality in the ChEMBL database

Papadatos, G., Gaulton, A., Hersey, A., & Overington, J. P. (2015) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-015-9860-5 · Cited by 169

Prediction of drug combination effects with a minimal set of experiments

Ianevski, A., Giri, A. K., Gautam, P., Kononov, A., Potdar, S., Saarela, J., Wennerberg, K., & Aittokallio, T. A. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0122-4 · Cited by 169

Comparison of the Predictive Performance and Interpretability of Random Forest and Linear Models on Benchmark Data Sets

Marchese Robinson, R. L., Palczewska, A., Palczewski, J., & Kidley, N. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00753 · Cited by 167

Drug Repositioning by Kernel-Based Integration of Molecular Structure, Molecular Activity, and Phenotype Data

Wang, Y., Chen, S., Deng, N.-Y., & Wang, Y. (2013) · PLoS ONE · doi:10.1371/journal.pone.0078518 · Cited by 165

Use of a Novel Artificial Intelligence Platform on Mobile Devices to Assess Dosing Compliance in a Phase 2 Clinical Trial in Subjects With Schizophrenia

Bain, E., Shafner, L., Walling, D. P., Othman, A. A., Chuang‐Stein, C., Hinkle, J. E., & Hanina, A. (2017) · JMIR mhealth and uhealth · doi:10.2196/mhealth.7030 · Cited by 163

Evaluating the molecule-based prediction of clinical drug responses in cancer

Ding, Z., Zu, S., & Gu, J. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw344 · Cited by 162

DeepDSC: A Deep Learning Method to Predict Drug Sensitivity of Cancer Cell Lines

Li, M., Wang, Y., Zheng, R., Shi, X. M., Li, Y., Wu, F., & Wang, J. (2019) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2019.2919581 · Cited by 162

Predicting CNS Permeability of Drug Molecules: Comparison of Neural Network and Support Vector Machine Algorithms

Doniger, S. W., Hofmann, T., & Yeh, J. I. (2002) · Journal of Computational Biology · doi:10.1089/10665270260518317 · Cited by 159

Predicting Synergism of Cancer Drug Combinations Using NCI-ALMANAC Data

Sidorov, P., Naulaerts, S., Ariey-Bonnet, J., Pasquier, E., & Ballester, P. J. (2019) · Frontiers in Chemistry · doi:10.3389/fchem.2019.00509 · Cited by 158

Experimental and Theoretical Observations of Aromaticity in Heterocyclic XAl3 (X=Si, Ge, Sn, Pb) Systems

Li, X., Zhang, H., Wang, L., Kuznetsov, A. E., Cannon, N. A., & Boldyrev, A. I. (2001) · Angewandte Chemie International Edition · doi:10.1002/1521-3773(20010518)40:10 · Cited by 157

Optimizing distributions over molecular space. An Objective-Reinforced Generative Adversarial Network for Inverse-design Chemistry (ORGANIC)Preprint

Sánchez-Lengeling, B., Outeiral, C., Guimaraes, G. L., & Aspuru‐Guzik, A. (2017) · ChemRxiv · doi:10.26434/chemrxiv.5309668.v2 · Cited by 156

Silymarin Ameliorates Acrylamide-Induced Hyperlipidemic Cardiomyopathy in Male Rats

Abdel-Moneim, A. M., Elsawy, H., Alzahrani, A. M., Ali, A. M., & Mahmoud, O. (2019) · BioMed Research International · doi:10.1155/2019/4825075 · Cited by 154

Deep Learning and Virtual Drug Screening

Carpenter, K. A., Cohen, D. S., Jarrell, J. T., & Huang, X. (2018) · Future Medicinal Chemistry · doi:10.4155/fmc-2018-0314 · Cited by 152

Prediction of passive blood–brain partitioning: Straightforward and effective classification models based on in silico derived physicochemical descriptors

Vilar, S., Chakrabarti, M., & Costanzi, S. (2010) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2010.03.010 · Cited by 149

What has virtual screening ever done for drug discovery?

Clark, D. E. (2008) · Expert Opinion on Drug Discovery · doi:10.1517/17460441.3.8.841 · Cited by 148

Regulation of ABC Transporters Blood–Brain Barrier

Miller, D. S. (2015) · Advances in cancer research · doi:10.1016/bs.acr.2014.10.002 · Cited by 146

A Simple Representation of Three-Dimensional Molecular Structure

Axen, S. D., Huang, X., Cáceres, E. L., Gendelev, L., Roth, B. L., & Keiser, M. J. (2017) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.7b00696 · Cited by 144

Predicting Penetration Across the Blood-Brain Barrier from Simple Descriptors and Fragmentation Schemes

Zhao, Y. H., Abraham, M. H., Ibrahim, A., Fish, P. V., Cole, S. M., Lewis, M. L., de Groot, M. J., & Reynolds, D. P. (2006) · Journal of Chemical Information and Modeling · doi:10.1021/ci600312d · Cited by 141

DrugE-Rank: improving drug–target interaction prediction of new candidate drugs or targets by ensemble learning to rank

Yuan, Q., Gao, J., Wu, D., Zhang, S., Mamitsuka, H., & Zhu, S. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw244 · Cited by 140

DriverML: a machine learning algorithm for identifying driver genes in cancer sequencing studies

Han, Y., Yang, J., Qian, X., Cheng, W., Liu, S., Hua, X., Zhou, L., Yang, Y., Wu, Q., Liu, P., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz096 · Cited by 140

Predicting functional effects of missense variants in voltage-gated sodium and calcium channels

Heyne, H. O., Baez-Nieto, D., Iqbal, S., Palmer, D. S., Brunklaus, A., May, P., Collaborative, E., Johannesen, K. M., Lauxmann, S., Lemke, J. R., et al. (2020) · Science Translational Medicine · doi:10.1126/scitranslmed.aay6848 · Cited by 140

Molecular fingerprint-based artificial neural networks QSAR for ligand biological activity predictions

Myint, K.-Z., Wang, L., Tong, Q., & Xie, X.-Q. (2012) · Molecular Pharmaceutics · doi:10.1021/mp300237z · Cited by 139

Machine Learning Methods for Protein Structure Prediction

Cheng, J., Tegge, A. N., & Baldi, P. (2008) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2008.2008239 · Cited by 139

DigSee: disease gene search engine with evidence sentences (version cancer)

Kim, J.-K., So, S., Lee, H., Park, J. C., Kim, J., & Lee, H. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt531 · Cited by 136

Tuning artificial intelligence on the de novo design of natural-product-inspired retinoid X receptor modulators

Merk, D., Grisoni, F., Friedrich, L., & Schneider, G. (2018) · Communications Chemistry · doi:10.1038/s42004-018-0068-1 · Cited by 136

Revealing the macromolecular targets of complex natural products

Reker, D., Perna, A. M., Rodrigues, T., Schneider, P., Reutlinger, M., Mönch, B., Koeberle, A., Lamers, C., Gabler, M., Steinmetz, H., et al. (2014) · Nature Chemistry · doi:10.1038/nchem.2095 · Cited by 135

In silico prediction of novel therapeutic targets using gene–disease association data

Ferrero, E., Dunham, I., & Sanséau, P. (2017) · Journal of Translational Medicine · doi:10.1186/s12967-017-1285-6 · Cited by 134

In Silico Prediction of Blood Brain Barrier Permeability: An Artificial Neural Network Model

Garg, P., & Verma, J. K. (2005) · Journal of Chemical Information and Modeling · doi:10.1021/ci050303i · Cited by 131

Determining druggability

Owens, J. (2007) · Nature Reviews Drug Discovery · doi:10.1038/nrd2275 · Cited by 130

Predictions of the ADMET Properties of Candidate Drug Molecules Utilizing Different QSAR/QSPR Modelling Approaches

Khan, M. T. H. (2010) · Current Drug Metabolism · doi:10.2174/138920010791514306 · Cited by 127

Application of ALOGPS 2.1 to Predict log D Distribution Coefficient for Pfizer Proprietary Compounds

Tetko, I. V., & Poda, G. I. (2004) · Journal of Medicinal Chemistry · doi:10.1021/jm049509l · Cited by 126

Quantitative structure–activity relationship: promising advances in drug discovery platforms

Wang, T., Wu, M., Lin, J., & Yang, L.-R. (2015) · Expert Opinion on Drug Discovery · doi:10.1517/17460441.2015.1083006 · Cited by 125

CHASM and SNVBox: toolkit for detecting biologically important single nucleotide mutations in cancer

Wong, W. C., Kim, D., Carter, H. K., Diekhans, M., Ryan, M., & Karchin, R. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr357 · Cited by 125

CanDrA: Cancer-Specific Driver Missense Mutation Annotation with Optimized Features

Mao, Y., Chen, H., Liang, H., Meric‐Bernstam, F., Mills, G. B., & Chen, K. (2013) · PLoS ONE · doi:10.1371/journal.pone.0077945 · Cited by 123

istar: A Web Platform for Large-Scale Protein-Ligand Docking

Li, H., Leung, K., Ballester, P. J., & Wong, M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0085678 · Cited by 123

iDTI-ESBoost: Identification of Drug Target Interaction Using Evolutionary and Structural Features with Boosting

Rayhan, F., Ahmed, S., Shatabda, S., Farid, D. M., Mousavian, Z., Dehzangi, A., & Rahman, M. S. (2017) · Scientific Reports · doi:10.1038/s41598-017-18025-2 · Cited by 121

2016 FDA drug approvals

Mullard, A. (2017) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2017.14 · Cited by 121

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Gupta, R., Srivastava, D., Sahu, M., Tiwari, S., Ambasta, R. K., & Kumar, P. (2021) · Molecular Diversity · doi:10.1007/s11030-021-10217-3 · Cited by 1,473

Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design

Vora, L. K., Gholap, A. D., Jetha, K. L., Singh, T. R. R., Solanki, H. K., & Chavda, V. P. (2023) · Pharmaceutics · doi:10.3390/pharmaceutics15071916 · Cited by 1,193

The role of AI in drug discovery: challenges, opportunities, and strategies

Blanco-González, A., Cabezón, A., Seco-González, A., Conde-Torres, D., Antelo-Riveiro, P., Piñeiro, Á., & Garcia-Fandino, R. (2023) · Pharmaceuticals · doi:10.3390/ph16060891 · Cited by 690

Using artificial intelligence in academic writing and research: An essential productivity tool

Khalifa, M., & Albadawy, M. (2024) · Computer Methods and Programs in Biomedicine Update · doi:10.1016/j.cmpbup.2024.100145 · Cited by 580

AI-Driven Drug Discovery: A Comprehensive Review

Ferreira, F. J. N., & Carneiro, A. S. (2025) · ACS Omega · doi:10.1021/acsomega.5c00549 · Cited by 202

Application of Computational Intelligence Through the Lens of CNS Drug Discovery: A Study of AI-Driven Predictive Models

Adhikary, S., Sasmal, D., Mukherjee, G., Banerjee, K., & Mallik, B. B. (2025) · Apple Academic Press eBooks · doi:10.1201/9781998511013-15

Advancement of Artificial Intelligence and Machine Learning in Drug Discovery for the Central Nervous System Diseases

Malviya, R., Sharma, R., Sridhar, S. B., & Kumar, S. (2026) · Apple Academic Press eBooks · doi:10.1201/9781779640819-9

Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system

Gautam, V., Gaurav, A., Masand, N., Lee, V. S., & Patil, V. M. (2022) · Molecular Diversity · doi:10.1007/s11030-022-10489-3 · Cited by 40

Recent trends in artificial intelligence-driven identification and development of anti-neurodegenerative therapeutic agents

Kashyap, K., & Siddiqi, M. I. (2021) · Molecular Diversity · doi:10.1007/s11030-021-10274-8 · Cited by 24

Applications of artificial intelligence in drug discovery for neurological diseases

Ekins, S., & Lane, T. R. (2025) · Neurotherapeutics · doi:10.1016/j.neurot.2025.e00624 · Cited by 12