3 citations · 7 across the 14 of their papers we have counts for
14 papers
How Well Can Frontier Large Language Models Generate Structures? High Quality Prediction of Molecular Geometries with Help from Fine-Tuning
Joseph M. Cavanagh, Jonathan B. Arnold, Giovanni Battista Alteri +2
The power of Large Language Models (LLMs) has led us to investigate how they might be fine-tuned for learning the "language of molecular geometry". The fine-tuning of the LLMs usin…
Agents' Last Exam
Yiyou Sun, Xinyang Han, Weichen Zhang +355
Recent AI systems have achieved strong results on a wide range of benchmarks, yet these gains have not translated into economically meaningful deployment across many professional d…
Towards Diverse Scientific Hypothesis Search with Large Language Models
Haorui Wang, Parshin Shojaee, Kazem Meidani +7
Large language models (LLMs) are on the rise for accelerating scientific discovery, most recently in advanced tasks such as generating valid scientific hypotheses. Yet in many disc…
DrugPlayGround: Benchmarking Large Language Models and Embeddings for Drug Discovery
Tianyu Liu, Sihan Jiang, Fan Zhang +3
Large language models (LLMs) are in the ascendancy for research in drug discovery, offering unprecedented opportunities to reshape drug research by accelerating hypothesis generati…
Machine-Learned Leftmost Hessian Eigenvectors for Robust Transition State Finding
Guanchen Wu, Chung-Yueh Yuan, Kareem Hegazy +2
The reliable determination of transition states (TSs) benefits from second-order information for robust convergence and validation, but the computational expense of Hessians prohib…
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…
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…
Deep Learning of Proteins with Local and Global Regions of Disorder
Oufan Zhang, Zi Hao Liu, Julie D Forman-Kay +1
Although machine learning has transformed protein structure prediction of folded protein ground states with remarkable accuracy, intrinsically disordered proteins and regions (IDPs…
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…
Biological Insights from Integrative Modeling of Intrinsically Disordered Protein Systems
Zi Hao Liu, Maria Tsanai, Oufan Zhang +2
Intrinsically disordered proteins and regions are increasingly appreciated for their abundance in the proteome and the many functional roles they play in the cell. In this short re…
Determining the Role of Electrostatics in the Making and Breaking of the Caprin1-ATP Nanocondensate
Maria Tsanai, Teresa Head-Gordon
We employ a multiscale computational approach to investigate the condensation process of the C-terminal low-complexity region of the Caprin1 protein as a function of increasing ATP…
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…
Computational Methods to Investigate Intrinsically Disordered Proteins and their Complexes
Zi Hao Liu, Maria Tsanai, Oufan Zhang +2
In 1999 Wright and Dyson highlighted the fact that large sections of the proteome of all organisms are comprised of protein sequences that lack globular folded structures under phy…
A Curated Rotamer Library for Common Post-Translational Modifications of Proteins
Oufan Zhang, Shubhankar A. Naik, Zi Hao Liu +2
Sidechain rotamer libraries of the common amino acids of a protein are useful for folded protein structure determination and for generating ensembles of intrinsically disordered pr…