ProtAgents: protein discovery <i>via</i> large language model multi-agent collaborations combining physics and machine learning-Reference-Cited by-同舟云学术

ProtAgents: protein discovery via large language model multi-agent collaborations combining physics and machine learning

Published:2024 Issue:7 Volume:3 Page:1389-1409
ISSN:2635-098X
Container-title:Digital Discovery
language:en
Short-container-title:Digital Discovery

Author:

Ghafarollahi Alireza¹,Buehler Markus J.¹²^ORCID

Affiliation:

1. Laboratory for Atomistic and Molecular Mechanics (LAMM), Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA

2. Center for Computational Science and Engineering, Schwarzman College of Computing, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA 02139, USA

Abstract

ProtAgents is a de novo protein design platform based on multimodal LLMs, where distinct AI agents with expertise in knowledge retrieval, protein structure analysis, physics-based simulations, and results analysis tackle tasks in a dynamic setting.

Funder

U.S. Department of Agriculture

U.S. Department of Energy

Strategic Environmental Research and Development Program

Army Research Office

National Institutes of Health

Office of Naval Research

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/DD/D4DD00013G

Reference98 articles.

1. The coming of age of de novo protein design

2. P.Notin , M.Dias , J.Frazer , J. M.Hurtado , A. N.Gomez , D.Marks and Y.Gal , Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval , 2022 , https://proceedings.mlr.press/v162/notin22a.html

3. K. E.Wu , K. K.Yang , R.vanden Berg , J. Y.Zou , A. X.Lu and A. P.Amini , arXiv , 2022 , preprint, arXiv:2209.15611v2, 10.48550/arXiv.2209.15611

4. N.Anand and T.Achim , arXiv , 2022 , preprint, arXiv:2205.15019, 10.48550/arXiv.2205.15019

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