activity
20212025
most citedLeak Proof PDBBind: A Reorganized Dataset of Protein-Ligand Complexes for More Generalizable Binding Affinity Prediction

23 citations · 26 across the 6 of their papers we have counts for

collaborators

7 papers

cs.AI2025

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…

physics.chem-ph2025★ 1 cited

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…

cs.LG2025★ 2 cited

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…

physics.bio-ph2024

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…

physics.chem-ph2024

SmileyLlama: Modifying Large Language Models for Directed Chemical Space Exploration

Joseph M. Cavanagh, Kunyang Sun, Andrew Gritsevskiy +4

We show that large language model (LLMs) can be transformed via supervised fine-tuning (SFT) of engineered prompts into SmileyLlama for exploring the chemical space of drug molecul…

physics.bio-ph2023★ 23 cited

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…

q-bio.BM2021

Mining for Potent Inhibitors through Artificial Intelligence and Physics: A Unified Methodology for Ligand Based and Structure Based Drug Design

Jie Li, Oufan Zhang, Yingze Wang +8

The viability of a new drug molecule is a time and resource intensive task that makes computer-aided assessments a vital approach to rapid drug discovery. Here we develop a machine…