Ligandability and druggability assessment via machine learning

Frontiers RASPD+: Fast Protein-Ligand Binding Free Energy Prediction Using Simplified Physicochemical Features

Applications of machine learning in computer-aided drug discovery, QRB Discovery

DrugnomeAI is an ensemble machine-learning framework for predicting druggability of candidate drug targets

Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition - ScienceDirect

Immunocure Discovery Solutions

PDF) Druggability Assessment in TRAPP using Machine Learning Approaches

Artificial Intelligence and Machine Learning - Psivant Therapeutics

PDF) Structure-based druggability assessment—identifying suitable targets for small molecule therapeutics

Best practices for AI and ML use across the drug discovery and development lifecycle - Clarivate

Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries

Spatiotemporal identification of druggable binding sites using deep learning

Sequence-based prediction of protein binding regions and drug–target interactions, Journal of Cheminformatics

Roadmap: Unlocking machine learning for drug discovery - Bessemer Venture Partners