Advancing Reproducibility and Open Data in Theoretical and Computational Chemistry

Rommie E. Amaro Victor Batista Jochen Blumberger Yee Siew Choong Clémence Corminboeuf Zoe Cournia Qiang Cui Marco De Vivo Francesco A. Evangelista Yi Qin Gao Debashree Ghosh Xiao He Olexandr Isayev Syma Khalid Johannes Kirchmair John R. Kitchin Haiyan Liu Kevin J. Naidoo Duc Nguyen Ariane Nunes Alves Giulia Palermo Brett Savoie Thereza A. Soares Pratyush Tiwary Guowei Wei Xiao Zheng Tong Zhu Kenneth M. Merz Jr. Laura Gagliardi
April 2026
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Abstract

This joint JCTC/JCIM editorial outlines practical expectations for reproducibility and open science in theoretical and computational chemistry, emphasizing FAIR data practices, transparent workflows, and clear data/software availability for original research manuscripts.

Type
Journal article
Publication
Journal of Chemical Theory and Computation, 22(9): 4199-4200
Reproducibility Open Data Computational Chemistry
Duc Nguyen
Duc Nguyen
Associate Professor of Mathematics

Duc Nguyen develops mathematical and AI frameworks for molecular bioscience, drug discovery, and scientific computing. His group blends differential geometry, graph theory, and machine learning to build high-fidelity models for biomolecular systems, with notable wins in the D3R Grand Challenges and collaborations with Pfizer and Bristol Myers Squibb. Supported by multiple NSF awards, he has advised students and postdocs across theory and applications of AI-driven drug design.