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Conformer Search Using SE3-Transformers and Imitation Learning
Luca Thiede · Santiago Miret · Krzysztof Sadowski · Haoping Xu · Mariano Phielipp · Alan Aspuru-Guzik

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in AI for Accelerated Materials Design (AI4Mat) »
Event URL: https://openreview.net/forum?id=b832-LQhwYP »

We introduce a novel approach to conformer search, the discovery of three-dimensional structures for two-dimensional molecular formulas. We focus on organic molecules using deep imitation learning and equivariant graph neural networks, with the prospect of using reinforcement learning algorithms for fine tuning. To that end, we present our interactive environment that describes the molecule in a ridig-rotor approximation and leverage a behavioral cloning torsion policy to autoregressively determine the dihedral angles of the molecule ultimately yielding a three-dimensional molecular structure. For our policy architecture, we leverage an SE(3) equivariant neural network, which enables us to exploit inherent molecular symmetries and to respect the topology of the angle distribution using a Mixture of Projected Normals action distribution. Our preliminary results for a policy trained on a behavioral cloning objective using the QM9 dataset for expert trajectories shows that the policy can accurately predict torsion angles for various molecules. We believe this to be a promising starting point for future work pertaining to performing conformer search using deep reinforcement learning.

Keywords: imitation learning · molecules · Conformer Search · Equivariant Neural Networks

Author Information

Luca Thiede (University of Toronto)
Santiago Miret (Intel AI Lab)
Krzysztof Sadowski
Haoping Xu (University of Toronto)
Mariano Phielipp (Intel AI Labs)

Dr. Mariano Phielipp works at the Intel AI Lab inside the Intel Artificial Intelligence Products Group. His work includes research and development in deep learning, deep reinforcement learning, machine learning, and artificial intelligence. Since joining Intel, Dr. Phielipp has developed and worked on Computer Vision, Face Recognition, Face Detection, Object Categorization, Recommendation Systems, Online Learning, Automatic Rule Learning, Natural Language Processing, Knowledge Representation, Energy Based Algorithms, and other Machine Learning and AI-related efforts. Dr. Phielipp has also contributed to different disclosure committees, won an Intel division award related to Robotics, and has a large number of patents and pending patents. He has published on NeuriPS, ICML, ICLR, AAAI, IROS, IEEE, SPIE, IASTED, and EUROGRAPHICS-IEEE Conferences and Workshops.

Alan Aspuru-Guzik (University of Toronto)

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