David H. Brookes

According to our database, David H. Brookes authored at least 7 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

BioNeMo Framework: a modular, high-performance library for AI model development in drug discovery.

Peter St. John, Dejun Lin, Polina Binder, Malcolm Greaves, Vega Shah, John St. John, Adrian Lange, Patrick Hsu, Rajesh Illango, Arvind Ramanathan, Anima Anandkumar, David H. Brookes, Akosua Busia, Abhishaike Mahajan, Stephen Malina, Neha Prasad, Sam Sinai, Lindsay Edwards, Thomas Gaudelet, Cristian Regep, Martin Steinegger, Burkhard Rost, Alexander Brace, Kyle Hippe, Luca Naef, Keisuke Kamata, George Armstrong, Kevin Boyd, Zhonglin Cao, Han-Yi Chou, Simon Chu, Allan dos Santos Costa, Sajad Darabi, Eric Dawson, Kieran Didi, Cong Fu, Mario Geiger, Michelle Gill, Darren Hsu, Gagan Kaushik, Maria Korshunova, Steven Kothen-Hill, Youhan Lee, Meng Liu, Micha Livne, Zachary McClure, Jonathan Mitchell, Alireza Moradzadeh, Ohad Mosafi, Youssef Nashed, Saee Paliwal, Yuxing Peng, Sara Rabhi, Farhad Ramezanghorbani, Danny Reidenbach, Camir Ricketts, Brian Roland, Kushal Shah, Tyler Shimko, Hassan Sirelkhatim, Savitha Srinivasan, Abraham C. Stern, Dorota Toczydlowska, Srimukh Prasad Veccham, Niccolò Alberto Elia Venanzi, Anton Vorontsov, Jared Wilber, Isabel Wilkinson, Wei Jing Wong, Eva Xue, Cory Ye, Xin Yu, Yang Zhang, Guoqing Zhou, Becca Zandstein, Christian Dallago, Bruno Trentini, Emine Küçükbenli, Timur Rvachov, Eddie Calleja, Johnny Israeli, Harry Clifford, Risto Haukioja, Nicholas Haemel, Kyle Tretina, Neha Tadimeti, Anthony B. Costa

CoRR, 2024

2023

Beyond the training set: an intuitive method for detecting distribution shift in model-based optimization.

Farhan Damani, David H. Brookes, Theodore Sternlieb, Cameron Webster, Stephen Malina, Rishi Jajoo, Kathy Lin, Sam Sinai

CoRR, 2023

Contrastive losses as generalized models of global epistasis.

David H. Brookes, Jakub Otwinowski, Sam Sinai

CoRR, 2023

2020

A view of estimation of distribution algorithms through the lens of expectation-maximization.

David H. Brookes, Akosua Busia, Clara Fannjiang, Kevin Murphy, Jennifer Listgarten

Proceedings of the GECCO '20: Genetic and Evolutionary Computation Conference, 2020

2019

Conditioning by adaptive sampling for robust design.

David H. Brookes, Hahnbeom Park, Jennifer Listgarten

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Design by adaptive sampling.

David H. Brookes, Jennifer Listgarten

CoRR, 2018

2017

PB-AM: An open-source, fully analytical linear poisson-boltzmann solver.

Lisa E. Felberg, David H. Brookes, Eng-Hui Yap, Elizabeth Jurrus, Nathan A. Baker, Teresa Head-Gordon

J. Comput. Chem., 2017