TY - CONF
T1 - ENERGY-INSPIRED MOLECULAR CONFORMATION OPTIMIZATION
AU - Guan, Jiaqi
AU - Qian, Wesley Wei
AU - Liu, Qiang
AU - Ma, Wei Ying
AU - Ma, Jianzhu
AU - Peng, Jian
N1 - Funding Information:
We thank all the reviewers for their feedbacks through out the review cycles of the manuscript. We also thank Yuanyi Zhong and Yunan Luo for many helpful discussions. This work was supported by U.S. National Science Foundation under grant no. 2019897 and U.S. Department of Energy award DE-SC0018420. J.P. acknowledges the support from the Sloan Research Fellowship and the NSF CAREER Award.
Publisher Copyright:
© 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.
PY - 2022
Y1 - 2022
N2 - This paper studies an important problem in computational chemistry: predicting a molecule's spatial atom arrangements, or a molecular conformation. We propose a neural energy minimization formulation that casts the prediction problem into an unrolled optimization process, where a neural network is parametrized to learn the gradient fields of an implicit conformational energy landscape. Assuming different forms of the underlying potential energy function, we can not only reinterpret and unify many of the existing models but also derive new variants of SE(3)-equivariant neural networks in a principled manner. In our experiments, these new variants show superior performance in molecular conformation optimization comparing to existing SE(3)-equivariant neural networks. Moreover, our energy-inspired formulation is also suitable for molecular conformation generation, where we can generate more diverse and accurate conformers comparing to existing baselines.
AB - This paper studies an important problem in computational chemistry: predicting a molecule's spatial atom arrangements, or a molecular conformation. We propose a neural energy minimization formulation that casts the prediction problem into an unrolled optimization process, where a neural network is parametrized to learn the gradient fields of an implicit conformational energy landscape. Assuming different forms of the underlying potential energy function, we can not only reinterpret and unify many of the existing models but also derive new variants of SE(3)-equivariant neural networks in a principled manner. In our experiments, these new variants show superior performance in molecular conformation optimization comparing to existing SE(3)-equivariant neural networks. Moreover, our energy-inspired formulation is also suitable for molecular conformation generation, where we can generate more diverse and accurate conformers comparing to existing baselines.
UR - http://www.scopus.com/inward/record.url?scp=85150387203&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85150387203&partnerID=8YFLogxK
M3 - Paper
AN - SCOPUS:85150387203
T2 - 10th International Conference on Learning Representations, ICLR 2022
Y2 - 25 April 2022 through 29 April 2022
ER -