Exploring protein–ligand binding affinity prediction with electron density-based geometric deep learning

Abstract

A deep learning approach centered on electron density is suggested for predicting the binding affility between proteins and ligands. The approach is thoroughly assessed using various pertinent benchmarks.

Geometric deep learning-guided Suzuki reaction conditions assessment for applications in medicinal chemistry

“…Deep learning methodologies, integrated into medicinal chemistry workflows, aim to expedite the DMTA cycle, thereby delivering superior molecules more rapidly. 29–31 While substantial research in machine learning applications has focused on the deployment of generative methods 32–36 and structure-based scoring functions for bioactivity prediction, 37–42 the development of machine learning methods for efficient synthesis planning of complex molecules has emerged as another challenge in the field of drug discovery. 43,44 Especially, graph-based machine learning methods, facilitating efficient learning on three-dimensional (3D) molecular models, have proven instrumental in various domains of chemistry.…”

High-throughput quantum theory of atoms in molecules (QTAIM) for geometric deep learning of molecular and reaction properties

“…Furthermore, GNNs suffer from poor out-of-domain (OOD) extrapolation and we probe whether QTAIM features can help alleviate this shortcoming. 27 We note one other study 29 that takes a somewhat similar approach to using QTAIM for geometric machine learning; our work differs by including benchmarks on standard cheminformatic datasets, testing on spin/charge-varying datasets, testing out-of-domain performance, and providing tools for generating and training QTAIM-informed geometric learning models for both molecules and reactions.…”