5 papers
Accelerating Scientific Discovery with Autonomous Goal-evolving Agents
Yuanqi Du, Botao Yu, Tianyu Liu +25
There has been unprecedented interest in developing agents that expand the boundary of scientific discovery, primarily by optimizing quantitative objective functions specified by s…
Leak Proof PDBBind: A Reorganized Dataset of Protein-Ligand Complexes for More Generalizable Binding Affinity Prediction
Jie Li, Xingyi Guan, Oufan Zhang +4
The majority of machine learning scoring functions used in drug discovery for predicting protein-ligand binding poses and affinities have been trained on the PDBBind dataset. Howev…
Improved Treatment of 1-4 interactions in Force Fields for Molecular Dynamics Simulations
Aalim S. Abdullah, Yingze Wang, Maximilian F. S. J. Menger +2
Traditional force fields commonly use a combination of bonded torsional terms and empirically scaled non-bonded interactions to capture 1-4 energies and forces of atoms separated b…
SynLlama: Generating Synthesizable Molecules and Their Analogs with Large Language Models
Kunyang Sun, Dorian Bagni, Joseph M. Cavanagh +6
Generative machine learning models for exploring chemical space have shown immense promise, but many molecules they generate are too difficult to synthesize, making them impractica…
A Workflow to Create a High-Quality Protein-Ligand Binding Dataset for Training, Validation, and Prediction Tasks
Yingze Wang, Kunyang Sun, Jie Li +4
Development of scoring functions (SFs) used to predict protein-ligand binding energies requires high-quality 3D structures and binding assay data for training and testing their par…