# TorsionNet: A Reinforcement Learning Approach to Sequential Conformer Search

arXiv: https://arxiv.org/abs/2006.07078 · HTML: https://mlsanity.com/paper/2006.07078/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2020.

## Built on by (most cited first, 15 of 15)

- [GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation](https://mlsanity.com/paper/2203.02923/) (2022)
- [Learning Gradient Fields for Molecular Conformation Generation](https://mlsanity.com/paper/2105.03902/) (2021)
- [Generating 3D Molecules for Target Protein Binding](https://mlsanity.com/paper/2204.09410/) (2022)
- [Inverse design of 3d molecular structures with conditional generative neural networks](https://mlsanity.com/paper/2109.04824/) (2021)
- [An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming](https://mlsanity.com/paper/2105.07246/) (2021)
- [Graph Neural Networks for Molecules](https://mlsanity.com/paper/2209.05582/) (2022)
- [Implicit Transfer Operator Learning: Multiple Time-Resolution Surrogates for Molecular Dynamics](https://mlsanity.com/paper/2305.18046/) (2023)
- [Towards equilibrium molecular conformation generation with GFlowNets](https://mlsanity.com/paper/2310.14782/) (2023)
- [Scalable Fragment-Based 3D Molecular Design with Reinforcement Learning](https://mlsanity.com/paper/2202.00658/) (2022)
- [Geometry Informed Tokenization of Molecules for Language Model Generation](https://mlsanity.com/paper/2408.10120/) (2024)
- [Molecular relaxation by reverse diffusion with time step prediction](https://mlsanity.com/paper/2404.10935/) (2024)
- [Socialbots on Fire: Modeling Adversarial Behaviors of Socialbots via Multi-Agent Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2110.10655/) (2021)
- [Graph Representation Learning for Interactive Biomolecule Systems](https://mlsanity.com/paper/2304.02656/) (2023)
- [Structure-Based Drug Design via 3D Molecular Generative Pre-training and Sampling](https://mlsanity.com/paper/2402.14315/) (2024)
- [Diffusion-Driven Generative Framework for Molecular Conformation Prediction](https://mlsanity.com/paper/2401.09451/) (2023)

## Abstract (arXiv)

Molecular geometry prediction of flexible molecules, or conformer search, is a long-standing challenge in computational chemistry. This task is of great importance for predicting structure-activity relationships for a wide variety of substances ranging from biomolecules to ubiquitous materials. Substantial computational resources are invested in Monte Carlo and Molecular Dynamics methods to generate diverse and representative conformer sets for medium to large molecules, which are yet intractable to chemoinformatic conformer search methods. We present TorsionNet, an efficient sequential conformer search technique based on reinforcement learning under the rigid rotor approximation. The model is trained via curriculum learning, whose theoretical benefit is explored in detail, to maximize a novel metric grounded in thermodynamics called the Gibbs Score. Our experimental results show that TorsionNet outperforms the highest scoring chemoinformatics method by 4x on large branched alkanes, and by several orders of magnitude on the previously unexplored biopolymer lignin, with applications in renewable energy.