Estimating protein–ligand interactions with geometric deep learning and mixture density models

Aarts EH and Laarhoven PJ 1989 Simulated annealing: an introduction. Stat. Neerl. 43 31–52

Article  Google Scholar 

Al-Rabeah MH and Lakizadeh A 2022 Prediction of drug-drug interaction events using graph neural networks based feature extraction. Sci. Rep. 12 15590

Article  CAS  PubMed  PubMed Central  Google Scholar 

Bilal Pant M, Zaheer H, et al. 2020 Differential evolution: A review of more than two decades of research. Eng. Appl. Artif. Intell. 90 103479

Article  Google Scholar 

D’Souza S, Prema KV and Balaji S 2020 Machine learning models for drug-target interactions: current knowledge and future directions. Drug Discov. Today 25 748–756

Article  CAS  PubMed  Google Scholar 

Dobson CM 2004 Chemical space and biology. Nature 432 824–828

Article  CAS  PubMed  Google Scholar 

Feng YH and Zhang SW 2022 Prediction of drug-drug interaction using an attention-based graph neural network on drug molecular graphs. Molecules 27 3004

Article  CAS  PubMed  PubMed Central  Google Scholar 

Friesner RA, Murphy RB, Repasky MP, et al. 2006 Extra precision glide: Docking and scoring incorporating a model of hydrophobic enclosure for protein−ligand complexes. J. Med. Chem. 49 6177–6196

Article  CAS  PubMed  Google Scholar 

Gainza P, Sverrisson F, Monti F, et al. 2020 Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning. Nature Methods 17 184–192

Article  CAS  PubMed  Google Scholar 

Grabowski SJ 2011 What is the covalency of hydrogen bonding? J. Chem. Rev. 111 2597–2625

Article  CAS  Google Scholar 

Grochowski P and Trylska J 2008 Continuum molecular electrostatics, salt effects, and counterion binding—a review of the Poisson-Boltzmann theory and its modifications. J. Biopolymers 89 93–113

Article  CAS  Google Scholar 

Howe TJ, Mahieu G, Marichal P, et al. 2007 Data reduction and representation in drug discovery. Drug Discov. Today 12 45–53

Article  CAS  PubMed  Google Scholar 

Jurrus E, Engel D, Star K, et al. 2018 Improvements to the APBS biomolecular solvation software suite. Protein Sci. 27 112–128

Article  CAS  PubMed  Google Scholar 

Kastritis PL and Bonvin AM 2013 On the binding affinity of macromolecular interactions: daring to ask why proteins interact. J. R. Soc. Interface 10 20120835

Article  PubMed  PubMed Central  Google Scholar 

Kimber TB, Chen Y and Volkamer A 2021 Deep Learning in Virtual Screening: Recent Applications and Developments. Int. J. Mol. Sci. 22 4435

Article  CAS  PubMed  PubMed Central  Google Scholar 

Lin Z, Akin H, Rao R, et al. 2023 Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379 1123–1130

Article  CAS  PubMed  Google Scholar 

Lindsay BG and Lesperance ML 1995 A review of semiparametric mixture models. J. Stat. Plan. Infer. 47 29–39

Article  Google Scholar 

Nussinov R, Zhang M, Liu Y, et al. 2022 AlphaFold, artificial intelligence (AI), and allostery. J. Phys. Chem 126 6372–6383

Article  CAS  Google Scholar 

Oldfield CJ, Meng J, Yang JY, et al. 2008 Flexible nets: disorder and induced fit in the associations of p53 and 14–3-3 with their partners. BMC Genom. 9 1–20

Article  Google Scholar 

Paul D, Sanap G, Shenoy S, et al. 2021 Artificial intelligence in drug discovery and development. Drug Discov. Today 26 80–93

Article  CAS  PubMed  Google Scholar 

Pinzi L and Rastelli G 2019 Molecular docking: shifting paradigms in drug discovery. Int. J. Mol. Sci. 20 4331

Article  CAS  PubMed  PubMed Central  Google Scholar 

Reiser P, Neubert M, Eberhard A, et al. 2022 Graph neural networks for materials science and chemistry. Commun. Mater. 3 93

Article  CAS  PubMed  PubMed Central  Google Scholar 

Rezaei MA, Li Y, Wu D, et al. 2022 Deep learning in drug design: Protein–ligand binding affinity prediction. IEEE/ACM Trans. Comput. Biol. Bioinform. 19 407–417

Article  CAS  PubMed  PubMed Central  Google Scholar 

Saikia S and Bordoloi M 2019 Molecular docking: challenges, advances and its use in drug discovery perspective. Curr. Drug Targets 20 501–521

Article  CAS  PubMed  Google Scholar 

Sanner MF, Olson AJ and Spehner JC 1996 Reduced surface: an efficient way to compute molecular surfaces. Biopolymers 38 305–320

Article  CAS  PubMed  Google Scholar 

Sarker IH 2021 Deep Learning: A comprehensive overview on techniques, taxonomy, applications and research directions. SN Comput. Sci. 2 420

Article  PubMed  PubMed Central  Google Scholar 

Scapin G 2006 Structural biology and drug discovery. Curr. Pharm. Des. 12 2087–2097

Article  CAS  PubMed  Google Scholar 

Storn R and Price K 1997 Differential evolution – A simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11 341–359

Article  Google Scholar 

Su M, Yang Q, Du Y, et al. 2019 Comparative asessment of scoring functions: The CASF-2016 update. J. Chem. Inf. Model. 59 895–913

Article  CAS  PubMed  Google Scholar 

Surade S and Blundell TL 2012 Structural biology and drug discovery of difficult targets: the limits of ligandability. Chem. Biol. 19 42–50

Article  CAS  PubMed  Google Scholar 

Torres PHM, Sodero ACR, Jofily P, et al. 2019 Key topics in molecular docking for drug design. Int. J. Mol. Sci. 20 4574

Article  CAS  PubMed  PubMed Central  Google Scholar 

Wang R, Fang X, Lu Y, et al. 2005 The PDBbind database: Methodologies and updates. J. Med. Chem. 48 4111–4119

Article  CAS  PubMed  Google Scholar 

Wen N, Liu G, Zhang J, et al. 2022 A fingerprints based molecular property prediction method using the BERT model. J. Cheminf. 14 71

Article  Google Scholar 

Yang KK, Wu Z, Bedbrook CN, et al. 2018 Learned protein embeddings for machine learning. Bioinformatics 34 2642–2648

Article  CAS  PubMed  PubMed Central  Google Scholar 

Yang S-Q, Zhang L-X, Ge Y-J, et al. 2023 In-silico target prediction by ensemble chemogenomic model based on multi-scale information of chemical structures and protein sequences. J. Cheminf. 15 48

Article  CAS 

留言 (0)

沒有登入
gif