New machine learning and physics-based scoring functions for drug discovery

Deep learning in drug discovery: an integrative review and future challenges

SQM2.20: Semiempirical quantum-mechanical scoring function yields DFT-quality protein–ligand binding affinity predictions in minutes

Conceptual DFT, machine learning and molecular docking as tools for predicting LD50 toxicity of organothiophosphates

Molecular modeling in drug discovery - ScienceDirect

Frontiers Multi-and many-objective optimization: present and future in de novo drug design

Frontiers Empirical Scoring Functions for Structure-Based Virtual Screening: Applications, Critical Aspects, and Challenges

PDF) Machine-Learning- and Knowledge-Based Scoring Functions Incorporating Ligand and Protein Fingerprints

PDF) New machine learning and physics-based scoring functions for drug discovery

PIGNet: a physics-informed deep learning model toward generalized drug–target interaction predictions - Chemical Science (RSC Publishing) DOI:10.1039/D1SC06946B

Prediction of high anti-angiogenic activity peptides in silico using a generalized linear model and feature selection

AI-Guided Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) Prediction