AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility

A semiempirical free energy force field with charge‐based desolvation

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AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading

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Highly accurate protein structure prediction for the human proteome

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Proteome-wide covalent ligand discovery in native biological systems

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Comprehensive Evaluation of Ten Docking Programs on a Diverse Set of Protein-Ligand Complexes: The Prediction Accuracy of Sampling Power and Scoring Power

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Role of computer-aided drug design in modern drug discovery

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Drug–target interaction prediction: databases, web servers and computational models

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Beware of Docking!

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Necrostatin-1 analogues: critical issues on the specificity, activity and in vivo use in experimental disease models

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Boosting docking-based virtual screening with deep learning.

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The bile acid receptor GPBAR-1 (TGR5) modulates integrity of intestinal barrier and immune response to experimental colitis

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

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An Activity-Guided Map of Electrophile-Cysteine Interactions in Primary Human T Cells

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Mechanism-based corrector combination restores ΔF508-CFTR folding and function

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Chiral gold nanoparticles enantioselectively rescue memory deficits in a mouse model of Alzheimer’s disease

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Drug–target affinity prediction using graph neural network and contact maps

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

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AutoDock4Zn: An Improved AutoDock Force Field for Small-Molecule Docking to Zinc Metalloproteins

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Oncogenic protein interfaces: small molecules, big challenges

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An integrative drug repositioning framework discovered a potential therapeutic agent targeting COVID-19

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A Force Field with Discrete Displaceable Waters and Desolvation Entropy for Hydrated Ligand Docking

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Some gating potentiators, including VX-770, diminish ΔF508-CFTR functional expression

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Phytochemicals as inhibitors of NF-κB for treatment of Alzheimer’s disease

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Design of small molecules that target metal-Aβ species and regulate metal-induced Aβ aggregation and neurotoxicity

Choi, J.-S., Braymer, J. J., Nanga, R. P. R., Ramamoorthy, A., & Lim, M. H. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1006091107 · Cited by 264

Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening

Cang, Z., Mu, L., & Wei, G.-W. (2018) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005929 · Cited by 257

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

Stereoselective amino acid synthesis by synergistic photoredox-pyridoxal radical biocatalysis

Cheng, L., Li, D., Mai, B. K., Bo, Z., Cheng, L., Liu, P., & Yang, Y. (2023) · Science · doi:10.1126/science.adg2420 · Cited by 253

Covalent docking using autodock: two-point attractor and flexible side chain methods.

Bianco, G., Forli, S., Goodsell, D. S., & Olson, A. J. (2016) · Protein Science · doi:10.1002/pro.2733 · Cited by 246

Structural basis of ketamine action on human NMDA receptors

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Identification and Characterization of the First Small Molecule Inhibitor of MDMX

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Stearoyl-CoA desaturase promotes liver fibrosis and tumor development in mice via a wnt positive-signaling loop by stabilization of low-density lipoprotein-receptor-related proteins 5 and 6

Lai, K. K. Y., Kweon, S.-M., Chi, F., Hwang, E., Kabe, Y., Higashiyama, R., Qin, L., Yan, R., Wu, R. P., Lai, K., et al. (2017) · Gastroenterology · doi:10.1053/j.gastro.2017.01.021 · Cited by 165

Mathematical deep learning for pose and binding affinity prediction and ranking in D3R Grand Challenges

Nguyen, D. D., Cang, Z., Wu, K., Wang, M., Cao, Y., & Wei, G.-W. (2019) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-018-0146-6 · Cited by 155

Theory and Applications of Covalent Docking in Drug Discovery: Merits and Pitfalls

Kumalo, H., Bhakat, S., & Soliman, M. (2015) · Molecules · doi:10.3390/molecules20021984 · Cited by 158

Leveraging Uncertainty in Machine Learning Accelerates Biological Discovery and Design

Hie, B., Bryson, B. D., & Berger, B. (2020) · Cell Systems · doi:10.1016/j.cels.2020.09.007 · Cited by 154

Application advances of deep learning methods for de novo drug design and molecular dynamics simulation

Bai, Q., Liu, S., Tian, Y., Xu, T., Banegas‐Luna, A. J., Pérez‐Sánchez, H., Huang, J., Liu, H., & Yao, X. (2022) · WIREs Computational Molecular Science · doi:10.1002/wcms.1581 · Cited by 156

Predicting Relative Protein Affinity of Novel Per- and Polyfluoroalkyl Substances (PFASs) by An Efficient Molecular Dynamics Approach

Cheng, W., & Ng, C. A. (2018) · Environmental Science & Technology · doi:10.1021/acs.est.8b01268 · Cited by 141

Myricanol rescues dexamethasone-induced muscle dysfunction Via a sirtuin 1-dependent mechanism

Shen, S., Liao, Q., Liu, J., Pan, R., Lee, S. M., & Lin, L. (2019) · Journal of Cachexia Sarcopenia and Muscle · doi:10.1002/jcsm.12393 · Cited by 142

CovalentDock: Automated covalent docking with parameterized covalent linkage energy estimation and molecular geometry constraints

Ouyang, X., Zhou, S., Su, C. T. T., Ge, Z., Li, R., & Kwoh, C. K. (2013) · Journal of Computational Chemistry · doi:10.1002/jcc.23136 · Cited by 134

A learning-based method for drug-target interaction prediction based on feature representation learning and deep neural network

Peng, J., Li, J., & Shang, X. (2020) · BMC Bioinformatics · doi:10.1186/s12859-020-03677-1 · Cited by 133

DeepDrug3D: Classification of ligand-binding pockets in proteins with a convolutional neural network

Pu, L., Govindaraj, R. G., Lemoine, J. M., Wu, H.-C., & Brylinski, M. (2019) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1006718 · Cited by 133

Understanding the challenges of protein flexibility in drug design.

Antunes, D. A., Devaurs, D., & Kavraki, L. E. (2015) · Expert Opinion on Drug Discovery · doi:10.1517/17460441.2015.1094458 · Cited by 131

Probiotics and the BSH-related cholesterol lowering mechanism: a Jekyll and Hyde scenario

Choi, S.-B., Lew, L.-C., Yeo, S.-K., Nair Parvathy, S., & Liong, M.-T. (2015) · Critical Reviews in Biotechnology · doi:10.3109/07388551.2014.889077 · Cited by 119

Reprogramming immunosuppressive myeloid cells facilitates immunotherapy for colorectal cancer

Lu, W., Yu, W., He, J., Liu, W., Yang, J., Lin, X., Zhang, Y., Wang, X., Jiang, W., Luo, J., et al. (2021) · EMBO Molecular Medicine · doi:10.15252/emmm.202012798 · Cited by 112

Drug–target interaction predication via multi-channel graph neural networks

Li, Y., Qiao, G., Wang, K., & Wang, G. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbab346 · Cited by 112

Delayed Treatment With 4-Methylpyrazole Protects Against Acetaminophen Hepatotoxicity in Mice by Inhibition of c-Jun n-Terminal Kinase

Akakpo, J. Y., Ramachandran, A., Duan, L., Schaich, M. A., Jaeschke, M. W., Freudenthal, B. D., Ding, W.-X., Rumack, B. H., & Jaeschke, H. (2019) · Toxicological Sciences · doi:10.1093/toxsci/kfz077 · Cited by 105

Machine learning consensus scoring improves performance across targets in structure-based virtual screening.

Ericksen, S. S., Wu, H., Zhang, H., Michael, L. A., Newton, M. A., Hoffmann, F. M., & Wildman, S. A. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00153 · Cited by 107

Predicting the accuracy of protein–ligand docking on homology models

Bordogna, A., Pandini, A., & Bonati, L. (2011) · Journal of Computational Chemistry · doi:10.1002/jcc.21601 · Cited by 107

Studies on Cross-linking of succinic acid with chitosan/collagen

Mitra, T., Sailakshmi, G., Gnanamani, A., & Mandal, A. B. (2013) · Materials Research · doi:10.1590/s1516-14392013005000059 · Cited by 104

Discovery of a New Four-Leaf Clover-Like Ligand as a Potent c-MYC Transcription Inhibitor Specifically Targeting the Promoter G-Quadruplex

Hu, M.-H., Wang, Y.-Q., Yu, Z.-Y., Hu, L.-N., Ou, T.-M., Chen, S.-B., Huang, Z.-S., & Tan, J.-H. (2018) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.7b01697 · Cited by 101

Peptide inhibitors of the Keap1–Nrf2 protein–protein interaction with improved binding and cellular activity

Hancock, R., Schaap, M., Pfister, H., & Wells, G. (2013) · Organic & Biomolecular Chemistry · doi:10.1039/c3ob40249e · Cited by 91

Isoform Selectivity of Adenylyl Cyclase Inhibitors: Characterization of Known and Novel Compounds

Brand, C. S., Hocker, H. J., Gorfe, A. A., Cavasotto, C. N., & Dessauer, C. W. (2013) · The Journal of Pharmacology and Experimental Therapeutics · doi:10.1124/jpet.113.208157 · Cited by 87

Fragment Informatics and Computational Fragment‐Based Drug Design: An Overview and Update

Sheng, C., & Zhang, W. (2013) · Medicinal Research Reviews · doi:10.1002/med.21255 · Cited by 80

Uni-Mol: A Universal 3D Molecular Representation Learning Framework

Zhou, G., Gao, Z., Ding, Q., Zheng, H., Xu, H., Wei, Z., Zhang, L., & Ke, G. (2022) · doi:10.26434/chemrxiv-2022-jjm0j · Cited by 81

Human leukocyte antigen B∗14:01 and B∗35:01 are associated with trimethoprim-sulfamethoxazole induced liver injury

Li, Y., Phillips, E. J., Dellinger, A., Nicoletti, P., Schutte, R., Li, D., Ostrov, D. A., Fontana, R. J., Watkins, P. B., Stolz, A., et al. (2021) · Hepatology · doi:10.1002/hep.31258 · Cited by 76

Probing the interaction of Rivastigmine Tartrate, an important Alzheimer’s drug, with serum albumin: Attempting treatment of Alzheimer’s disease

Shamsi, A., Mohammad, T., Anwar, S., Alajmi, M. F., Hussain, A., Hassan, M. I., Ahmad, F., & Islam, A. (2020) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2020.01.134 · Cited by 73

Natural Product Anacardic Acid from Cashew Nut Shells Stimulates Neutrophil Extracellular Trap Production and Bactericidal Activity

Hollands, A., Corriden, R., Gysler, G., Dahesh, S., Olson, J., Raza Ali, S., Kunkel, M. T., Lin, A. E., Forli, S., Newton, A. C., et al. (2016) · Journal of Biological Chemistry · doi:10.1074/jbc.m115.695866 · Cited by 73

Antifungal Drug Repurposing

Kim, J. H., Cheng, L. W., Chan, K. L., Tam, C. C., Mahoney, N., Friedman, M., Shilman, M. M., & Land, K. M. (2020) · Antibiotics · doi:10.3390/antibiotics9110812 · Cited by 71

Ubiquinone-binding site mutagenesis reveals the role of mitochondrial complex II in cell death initiation

Kluckova, K., Sticha, M., Cerny, J., Mracek, T., Dong, L., Drahota, Z., Gottlieb, E., Neuzil, J., & Rohlena, J. (2015) · Cell Death & Disease · doi:10.1038/cddis.2015.110 · Cited by 66

Hybrid Multifunctional Modulators Inhibit Multifaceted Aβ Toxicity and Prevent Mitochondrial Damage

Rajasekhar, K., Mehta, K., & Govindaraju, T. (2018) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.8b00033 · Cited by 69

Retromer stabilization results in neuroprotection in a model of Amyotrophic Lateral Sclerosis

Muzio, L., Sirtori, R., Gornati, D., Eleuteri, S., Fossaghi, A., Brancaccio, D., Manzoni, L., Ottoboni, L., Feo, L. D., Quattrini, A., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17524-7 · Cited by 65

Cross-docking study on InhA inhibitors: a combination of Autodock Vina and PM6-DH2 simulations to retrieve bio-active conformations

Stigliani, J.-L., Bernardes-Génisson, V., Bernadou, J., & Pratviel, G. (2012) · Organic & Biomolecular Chemistry · doi:10.1039/c2ob25602a · Cited by 62

Autoinducer-2 and bile salts induce c-di-GMP synthesis to repress the T3SS via a T3SS chaperone

Li, S., Sun, H., Li, J., Zhao, Y., Wang, R., Xu, L., Duan, C., Li, J., Wang, Z., Liu, Q., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34607-9 · Cited by 58

Polymerization of the backbone of the pectic polysaccharide rhamnogalacturonan I

Amos, R. A., Atmodjo, M. A., Huang, C., Gao, Z., Venkat, A., Taujale, R., Kannan, N., Moremen, K. W., & Mohnen, D. (2022) · Nature Plants · doi:10.1038/s41477-022-01270-3 · Cited by 55

Eeyarestatin Compounds Selectively Enhance Sec61-Mediated Ca2+ Leakage from the Endoplasmic Reticulum

Gamayun, I., O'Keefe, S., Pick, T., Klein, M.-C., Nguyen, D., McKibbin, C., Piacenti, M., Williams, H. M., Flitsch, S. L., Whitehead, R. C., et al. (2019) · Cell Chemical Biology · doi:10.1016/j.chembiol.2019.01.010 · Cited by 51

Insights from incorporating quantum computing into drug design workflows

Lau, B., Emani, P. S., Chapman, J., Yao, L., Lam, T., Merrill, P., Warrell, J., Gerstein, M. B., & Lam, H. Y. K. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btac789 · Cited by 54

Adenine Nucleotide Translocase Is Acetylated in Vivo in Human Muscle: Modeling Predicts a Decreased ADP Affinity and Altered Control of Oxidative Phosphorylation

Mielke, C., Lefort, N., McLean, C. G., Cordova, J. M., Langlais, P. R., Bordner, A. J., Te, J. A., Ozkan, S. B., Willis, W. T., & Mandarino, L. J. (2014) · Biochemistry · doi:10.1021/bi401651e · Cited by 53

Disentangling the ecotoxicological selectivity of clove essential oil against aphids and non-target ladybeetles

Toledo, P. F. S., Viteri Jumbo, L. O., Rezende, S. M., Haddi, K., Silva, B. A., Mello, T. S., Della Lucia, T. M. C., Aguiar, R. W. S., Smagghe, G., & Oliveira, E. E. (2020) · Science of The Total Environment · doi:10.1016/j.scitotenv.2020.137328 · Cited by 52

Myricanol mitigates lipid accumulation in 3T3-L1 adipocytes and high fat diet-fed zebrafish via activating AMP-activated protein kinase

Shen, S., Liao, Q., Feng, Y., Liu, J., Pan, R., Lee, S. M.-Y., & Lin, L. (2019) · Food Chemistry · doi:10.1016/j.foodchem.2018.07.117 · Cited by 51

TG2 regulates the heat‐shock response by the post‐translational modification of HSF1

Rossin, F., Villella, V. R., D'Eletto, M., Farrace, M. G., Esposito, S., Ferrari, E., Monzani, R., Occhigrossi, L., Pagliarini, V., Sette, C., et al. (2018) · The EMBO Reports · doi:10.15252/embr.201745067 · Cited by 46

New Hybrids of 4-Amino-2,3-polymethylene-quinoline and p-Tolylsulfonamide as Dual Inhibitors of Acetyl- and Butyrylcholinesterase and Potential Multifunctional Agents for Alzheimer’s Disease Treatment

Makhaeva, G. F., Kovaleva, N. V., Boltneva, N. P., Lushchekina, S. V., Astakhova, T. Y., Rudakova, E. V., Proshin, A. N., Serkov, I. V., Radchenko, E. V., Palyulin, V. A., et al. (2020) · Molecules · doi:10.3390/molecules25173915 · Cited by 53

On-the-fly QM/MM docking with attracting cavities.

Chaskar, P., Zoete, V., & Röhrig, U. F. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00406 · Cited by 46

Identification of binding sites contributing to volatile anesthetic effects on GABA type A receptors

Woll, K. A., Zhou, X., Bhanu, N. V., Garcia, B. A., Covarrubias, M., Miller, K. W., & Eckenhoff, R. G. (2018) · The FASEB Journal · doi:10.1096/fj.201701347r · Cited by 38

Ranking power of the SQM/COSMO scoring function on carbonic anhydrase II-inhibitor complexes.

Pecina, A., Brynda, J., Vrzal, L., Gnanasekaran, R., Hořejší, M., Eyrilmez, S. M., Řezáč, J., Lepšík, M., Řezáčová, P., Hobza, P., et al. (2018) · ChemPhysChem · doi:10.1002/cphc.201701104 · Cited by 36

A Novel N-Methyltransferase in Arabidopsis Appears to Feed a Conserved Pathway for Nicotinate Detoxification among Land Plants and Is Associated with Lignin Biosynthesis

Li, W., Zhang, F., Wu, R., Jia, L., Li, G., Guo, Y., Liu, C., & Wang, G. (2017) · Plant Physiology · doi:10.1104/pp.17.00259 · Cited by 34

1,2,4-triazolo[1,5-a]pyrimidines as a novel class of inhibitors of the HIV-1 reverse transcriptase-associated ribonuclease H activity

Desantis, J., Massari, S., Corona, A., Astolfi, A., Sabatini, S., Manfroni, G., Palazzotti, D., Cecchetti, V., Pannecouque, C., Tramontano, E., et al. (2020) · Molecules · doi:10.3390/molecules25051183 · Cited by 29

Characterization of drug binding within the HCN1 channel pore

Tanguay, J., Callahan, K. M., & D’Avanzo, N. (2019) · Scientific Reports · doi:10.1038/s41598-018-37116-2 · Cited by 29

An allosteric propofol-binding site in kinesin disrupts kinesin-mediated processive movement on microtubules

Woll, K. A., Guzik-Lendrum, S., Bensel, B. M., Bhanu, N. V., Dailey, W. P., Garcia, B. A., Gilbert, S. P., & Eckenhoff, R. G. (2018) · Journal of Biological Chemistry · doi:10.1074/jbc.ra118.002182 · Cited by 23

Synthetic bovine lactoferrin peptide Lfampin kills Entamoeba histolytica trophozoites by necrosis and resolves amoebic intracecal infection in mice

Díaz-Godínez, C., González-Galindo, X., Meza-Menchaca, T., Bobes, R. J., de la Garza, M., León-Sicairos, N., Laclette, J. P., & Carrero, J. C. (2019) · Bioscience Reports · doi:10.1042/bsr20180850 · Cited by 23

A quantum mechanics-based halogen bonding scoring function for protein-ligand interactions.

Yang, Z., Liu, Y., Chen, Z., Xu, Z., Shi, J., Chen, K., & Zhu, W. (2015) · Journal of Molecular Modeling · doi:10.1007/s00894-015-2681-6 · Cited by 23

Ecdysteroid derivatives that reverse P-glycoprotein-mediated drug resistance

Bortolozzi, R., Luraghi, A., Mattiuzzo, E., Sacchetti, A., Silvani, A., & Viola, G. (2020) · Journal of Natural Products · doi:10.1021/acs.jnatprod.0c00334 · Cited by 21

Mutational Analysis of Sigma-1 Receptor’s Role in Synaptic Stability

Ryskamp, D. A., Zhemkov, V., & Bezprozvanny, I. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.01012 · Cited by 21

The Valorization of Spent Coffee Ground Extract as a Prospective Insecticidal Agent against Some Main Key Pests of Phaseolus vulgaris in the Laboratory and Field

Hussein, H., Abouamer, W., Ali, H., Elkhadragy, M., Yehia, H., & Farouk, A. (2022) · Plants · doi:10.3390/plants11091124 · Cited by 20

Synthesis and testing of novel alternative oxidase (AOX) inhibitors with antifungal activity against Moniliophthora perniciosa (Stahel), the causal agent of witches' broom disease of cocoa, and other phytopathogens

Barsottini, M. R., Pires, B. A., Vieira, M. L., Pereira, J. G., Costa, P. C., Sanitá, J., Coradini, A., Mello, F., Marschalk, C., Silva, E. M., et al. (2019) · Pest Management Science · doi:10.1002/ps.5243 · Cited by 19

Bioactive prenylated phenolic compounds from the aerial parts of Glycyrrhiza uralensis

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Combined Physics- and Machine-Learning-Based Method to Identify Druggable Binding Sites Using SILCS-Hotspots

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