4 papers
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…
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…
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…