Diffusion-based Generative AI for Exploring Transition States from 2D Molecular Graphs

• URL: http://arxiv.org/abs/2304.12233v3
• Date: Thu, 12 Oct 2023 12:00:09 GMT
• Title: Diffusion-based Generative AI for Exploring Transition States from 2D Molecular Graphs
• Authors: Seonghwan Kim, Jeheon Woo, Woo Youn Kim
• Abstract summary: We propose a generative approach based on the diffusion method, namely TSDiff, for prediction of transition state geometries. TSDiff outperformed the existing machine learning models with 3D geometries in terms of both accuracy and efficiency.
• Score: 0.3759936323189417
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The exploration of transition state (TS) geometries is crucial for elucidating chemical reaction mechanisms and modeling their kinetics. Recently, machine learning (ML) models have shown remarkable performance for prediction of TS geometries. However, they require 3D conformations of reactants and products often with their appropriate orientations as input, which demands substantial efforts and computational cost. Here, we propose a generative approach based on the stochastic diffusion method, namely TSDiff, for prediction of TS geometries just from 2D molecular graphs. TSDiff outperformed the existing ML models with 3D geometries in terms of both accuracy and efficiency. Moreover, it enables to sample various TS conformations, because it learned the distribution of TS geometries for diverse reactions in training. Thus, TSDiff was able to find more favorable reaction pathways with lower barrier heights than those in the reference database. These results demonstrate that TSDiff shows promising potential for an efficient and reliable TS exploration.

Related papers

• Geometric Trajectory Diffusion Models [58.853975433383326]
  Generative models have shown great promise in generating 3D geometric systems. Existing approaches only operate on static structures, neglecting the fact that physical systems are always dynamic in nature. We propose geometric trajectory diffusion models (GeoTDM), the first diffusion model for modeling the temporal distribution of 3D geometric trajectories.
  arXiv  Detail & Related papers  (2024-10-16T20:36:41Z)
• Aero-Nef: Neural Fields for Rapid Aircraft Aerodynamics Simulations [1.1932047172700866]
  This paper presents a methodology to learn surrogate models of steady state fluid dynamics simulations on meshed domains. The proposed models can be applied directly to unstructured domains for different flow conditions. Remarkably, the method can perform inference five order of magnitude faster than the high fidelity solver on the RANS transonic airfoil dataset.
  arXiv  Detail & Related papers  (2024-07-29T11:48:44Z)
• Automated 3D Pre-Training for Molecular Property Prediction [54.15788181794094]
  We propose a novel 3D pre-training framework (dubbed 3D PGT) It pre-trains a model on 3D molecular graphs, and then fine-tunes it on molecular graphs without 3D structures. Extensive experiments on 2D molecular graphs are conducted to demonstrate the accuracy, efficiency and generalization ability of the proposed 3D PGT.
  arXiv  Detail & Related papers  (2023-06-13T14:43:13Z)
• Geometric Latent Diffusion Models for 3D Molecule Generation [172.15028281732737]
  Generative models, especially diffusion models (DMs), have achieved promising results for generating feature-rich geometries. We propose a novel and principled method for 3D molecule generation named Geometric Latent Diffusion Models (GeoLDM)
  arXiv  Detail & Related papers  (2023-05-02T01:07:22Z)
• Orthogonal Matrix Retrieval with Spatial Consensus for 3D Unknown-View Tomography [58.60249163402822]
  Unknown-view tomography (UVT) reconstructs a 3D density map from its 2D projections at unknown, random orientations. The proposed OMR is more robust and performs significantly better than the previous state-of-the-art OMR approach.
  arXiv  Detail & Related papers  (2022-07-06T21:40:59Z)
• Surface Vision Transformers: Attention-Based Modelling applied to Cortical Analysis [8.20832544370228]
  We introduce a domain-agnostic architecture to study any surface data projected onto a spherical manifold. A vision transformer model encodes the sequence of patches via successive multi-head self-attention layers. Experiments show that the SiT generally outperforms surface CNNs, while performing comparably on registered and unregistered data.
  arXiv  Detail & Related papers  (2022-03-30T15:56:11Z)
• Non-equilibrium molecular geometries in graph neural networks [2.6040244706888998]
  Graph neural networks have become a powerful framework for learning complex structure-property relationships. Recently proposed methods have demonstrated that using 3D geometry information of the molecule along with the bonding structure can lead to more accurate prediction on a wide range of properties.
  arXiv  Detail & Related papers  (2022-03-07T20:20:52Z)
• Geometry-Contrastive Transformer for Generalized 3D Pose Transfer [95.56457218144983]
  The intuition of this work is to perceive the geometric inconsistency between the given meshes with the powerful self-attention mechanism. We propose a novel geometry-contrastive Transformer that has an efficient 3D structured perceiving ability to the global geometric inconsistencies. We present a latent isometric regularization module together with a novel semi-synthesized dataset for the cross-dataset 3D pose transfer task.
  arXiv  Detail & Related papers  (2021-12-14T13:14:24Z)
• GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles [60.12186997181117]
  Prediction of a molecule's 3D conformer ensemble from the molecular graph holds a key role in areas of cheminformatics and drug discovery. Existing generative models have several drawbacks including lack of modeling important molecular geometry elements. We propose GeoMol, an end-to-end, non-autoregressive and SE(3)-invariant machine learning approach to generate 3D conformers.
  arXiv  Detail & Related papers  (2021-06-08T14:17:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.