Great computational effort is invested in generating equilibrium states for molecular systems using, for example, Markov chain Monte Carlo. We present a probabilistic model that generates statistically independent samples for molecules from their graph representations. Our model learns a low-dimensional manifold that p…
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48 results for “molecular geometry”
Neural network learns from higher-order connections in molecules.
problem Graph neural networks fail to account for local and hidden structures in graphs.
method Developed a neural network that can pass messages and aggregate information across higher-order paths.
result The model improves molecular property prediction.
New method uses Riemannian geometry to describe molecular shapes.
problem Predicting drug-like molecules using shape similarity.
method Riemannian geometry applied to molecular surfaces.
result RGMolSA method captures molecular shape effectively.
New method generates molecular conformations efficiently.
problem Generating accurate molecular conformations efficiently.
method Variational approximation of rotatable bond torsion angles as a mixture of von Mises distributions.
result VonMisesNet generates conformations orders of magnitude faster than existing methods.
New method uses Riemannian geometry to quantify molecular shapes.
problem Quantifying molecular similarity for drug discovery.
method Riemannian geometry and Kähler quantization (KQMolSA).
result KQMolSA method compares well to existing shape similarity methods.
VecMol generates 3D molecules as continuous vector fields, overcoming modality and geometry constraints.
problem Challenges in generating 3D molecules, especially in drug discovery and materials science.
method VecMol reimagines molecular representation by modeling 3D molecules as continuous vector fields over Euclidean space, parameterized by a neural field and generated using a latent diffusion model.
result Vector-field-based representations show promise for 3D molecular generation, validated on benchmarks.
GCPNet improves molecular graph learning for protein structure and binding.
problem Learning from 3D molecular graphs for protein structure and binding.
method SE(3)-equivariant graph neural network for 3D molecular graphs.
result GCPNet achieves state-of-the-art performance in multiple molecular tasks.
Photo-induced processes are fundamental in nature, but accurate simulations are seriously limited by the cost of the underlying quantum chemical calculations, hampering their application for long time scales. Here we introduce a method based on machine learning to overcome this bottleneck and enable accurate photodynam…
Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentialsphysics.comp-ph
New method uses geometric moments for accurate machine learning potentials.
problem Creating high-dimensional potential energy surfaces efficiently.
method Feed-forward neural networks with invariant local molecular descriptors based on geometric moments.
result Accuracy comparable to established models, high efficiency.
Evaluation of the MACE Force Field Architecture: from Medicinal Chemistry to Materials Sciencephysics.chem-ph
MACE architecture outperforms alternatives in various molecular and materials science tasks.
problem Improving machine learning force fields for diverse molecular and materials science applications.
method Evaluation of MACE architecture on various datasets and tasks, demonstrating data efficiency and excellent performance.
result MACE architecture generally outperforms alternatives across a wide range of systems, including amorphous carbon, universal materials modelling, and organic chemistry.
Cormorant: Covariant Molecular Neural Networksphysics.comp-ph
We propose Cormorant, a rotationally covariant neural network architecture for learning the behavior and properties of complex many-body physical systems. We apply these networks to molecular systems with two goals: learning atomic potential energy surfaces for use in Molecular Dynamics simulations, and learning ground…
TorsionNet uses reinforcement learning to efficiently generate conformers of flexible molecules.
problem Efficiently generating diverse and representative conformer sets for flexible molecules.
method Sequential conformer search technique based on reinforcement learning under the rigid rotor approximation, trained via curriculum learning.
result TorsionNet outperforms chemoinformatics methods by 4x on large branched alkanes and several orders of magnitude on biopolymer lignin.
Riemannian geometry improves protein dynamics analysis.
problem Efficient analysis of protein dynamics data in non-linear spaces.
method Developed a local approximation technique for geodesics and a smooth manifold of protein conformations.
result Geodesics approximate molecular dynamics trajectories and provide realistic summary statistics.
GCDM generates valid large 3D molecules and optimizes existing molecules.
problem Lack of geometric properties in 3D molecule generation models.
method Introduces Geometry-Complete Diffusion Model (GCDM) using equivariant GNNs.
result Significantly outperforms existing models in 3D molecule generation and optimization.
A set of molecular descriptors whose length is independent of molecular size is developed for machine learning models that target thermodynamic and electronic properties of molecules. These features are evaluated by monitoring performance of kernel ridge regression models on well-studied data sets of small organic mole…
Persistent homology provides a new, efficient molecular descriptor for protein dynamics.
problem Designing effective molecular descriptors for high-dimensional MD trajectories.
method Introduced masked Flood complex, a protein-tailored modification of simplicial complexes, for persistent homology.
result Persistent homology-based descriptors are competitive across protein dynamics tasks, including frame-level observable regression and MSM estimation.
Bayesian quadrature improves integration on Riemannian manifolds.
problem Efficiently computing integrals on nonlinear geometric data.
method Probabilistic numerical methods, specifically Bayesian quadrature, on Riemannian manifolds.
result Bayesian quadrature reduces the number of function evaluations compared to Monte Carlo methods.
Machine learning methods have shown promise in predicting molecular properties, and given sufficient training data machine learning approaches can enable rapid high-throughput virtual screening of large libraries of compounds. Graph-based neural network architectures have emerged in recent years as the most successful …
TALBO optimizes latent spaces for evolving design objectives.
problem Temporal drift in design objectives.
method GP-prior variational autoencoder for time-varying latent space.
result Consistently outperforms LSBO baselines across varying drift speeds and objectives.
New method detects and compares folding pathways of knotted proteins.
problem Understanding the function of knots in protein folding.
method Topological analysis of protein knotoid distributions and entanglement.
result Reveals unique folding pathway for shallow knotted Carbonic Anhydrases.
A molecule's geometry, also known as conformation, is one of a molecule's most important properties, determining the reactions it participates in, the bonds it forms, and the interactions it has with other molecules. Conventional conformation generation methods minimize hand-designed molecular force field energy functi…
A new model designs molecular latent vectors for drug discovery.
problem Designing effective molecular descriptors from molecular structures.
method Proposes a denoising diffusion probabilistic model (DDPM) for variational autoencoding molecular graphs.
result Demonstrates superior prediction performance and robustness compared to existing approaches.
MoFlow generates chemically valid molecular graphs from latent representations.
problem Generating chemically valid molecular graphs from latent representations is challenging.
method MoFlow uses a flow-based approach with Glow for bond generation and a novel graph conditional flow for atom generation, ensuring chemical validity and efficiency.
result MoFlow achieves state-of-the-art performance in molecular graph generation and optimization.
Study compares GNNs and classical molecular featurisations for molecular property and cliff prediction.
problem Comparing GNNs and classical featurisations for molecular property and cliff prediction.
method Systematic exploration and comparison of PDVs, ECFPs, and GNNs; introduction of substructure pooling.
result Sort & Slice outperforms hash-based folding in ECFP vectorization.
Molecular generative model based on conditional variational autoencoder for de novo molecular designcs.LG
We propose a molecular generative model based on the conditional variational autoencoder for de novo molecular design. It is specialized to control multiple molecular properties simultaneously by imposing them on a latent space. As a proof of concept, we demonstrate that it can be used to generate drug-like molecules w…
Novel RL approach for molecular design using quantum mechanics.
problem Existing RL methods for molecular design are limited in scope and reward function.
method Formulation in Cartesian coordinates, direct use of quantum mechanics for reward function, translation and rotation invariant state-action space.
result Agent efficiently learns to solve molecular design tasks from scratch.
Generative models are becoming a tool of choice for exploring the molecular space. These models learn on a large training dataset and produce novel molecular structures with similar properties. Generated structures can be utilized for virtual screening or training semi-supervised predictive models in the downstream tas…
We study the problem of determining the least symmetric triangle, which arises both from pure geometry and from the study of molecular chirality in chemistry. Using the correspondence between planar -gons and points in the Grassmannian of 2-planes in real -space introduced by Hausmann and Knutson, this correspond…
Molecular Latent Space Simulatorsphysics.comp-ph
LSS learns molecular trajectories from MD data.
problem Limited integration time steps in MD simulations.
method Three deep learning networks for slow collective variables, dynamics, and configuration reconstruction.
result Generates ultra-long synthetic folding trajectories.
AniDS improves molecular force field modeling by learning anisotropic noise.
problem Molecular force field modeling suffers from oversimplified assumptions about atomic motions.
method AniDS introduces anisotropic noise generation for better modeling of directional and structural variability.
result AniDS outperforms existing methods on benchmarks, achieving significant improvements in force prediction accuracy.
Machine learning models simulate molecular spectra and reactions in solvents.
problem Accurate simulation of molecular spectra and reactions in solvent environments.
method Introduced FieldSchNet, a deep neural network for modeling molecular interactions with external fields.
result Demonstrated significant lowering of Claisen rearrangement reaction activation barrier using FieldSchNet.
Framework for training-free guidance in discrete diffusion models for molecular generation.
problem No equivalent training-free guidance methods for discrete diffusion models.
method Framework using guidance functions for discrete data.
result Demonstrated utility on molecular graph generation tasks.
Optimizes molecular generation for chemist preferences.
problem Models lack inherent preferences for chemist-desired structures.
method Fine-tuning with Direct Preference Optimization.
result Approach is simple, efficient, and highly effective.
New RL method designs 3D molecules with improved symmetry.
problem Lack of 3D information in molecular design.
method Symmetry-aware actor-critic architecture using spherical harmonics.
result Improves generalization and molecule quality.
Transferable Generative Models Bridge Femtosecond to Nanosecond Time-Step Molecular Dynamicsphysics.chem-ph
Generative models accelerate molecular dynamics by four orders of magnitude.
problem Femtosecond time steps limit access to slow molecular processes.
method Deep generative modeling framework that accelerates sampling.
result Quantitative characterization of equilibrium ensembles and dynamical relaxation processes.
FlowMO uses Gaussian Processes for molecular property prediction with uncertainty.
problem Predicting molecular properties with uncertainty for small datasets.
method Gaussian Processes implemented in FlowMO, built on GPflow and RDKit.
result Comparable predictive performance to deep learning but superior uncertainty calibration.
The paper extends manifold learning to arbitrary norms, improving molecular motion mapping.
problem Improving manifold learning for non-Euclidean norms.
method Determines the limiting differential operator for graph Laplacians using any norm.
result A modified Laplacian eigenmaps algorithm using Earthmover's distance outperforms Euclidean methods in molecular motion mapping.
The process of morphogenesis, which can be defined as an evolution of the form of an organism, is one of the most intriguing mysteries in the life sciences. It is clear, that gene expression patterns cannot explain the development of the precise geometry of an organism and its parts in space. Here, we suggest a set of …
Implicit Transfer Operator Learning: Multiple Time-Resolution Surrogates for Molecular Dynamicsphysics.chem-ph
Framework learns surrogates for molecular dynamics across multiple time-scales.
problem Stable molecular dynamics simulations require small time-steps, but long-time-scale moments need repeated simulations.
method Implicit Transfer Operator Learning with denoising diffusion probabilistic models and SE(3) equivariant architecture.
result Models can generate self-consistent stochastic dynamics across multiple time-scales.
Paper improves molecular property prediction using denoising autoencoders.
problem Limited data for molecular property prediction from 3D structures.
method Pre-training via denoising for learning molecular force fields.
result Achieves new state-of-the-art performance on QM9 dataset.
Molecular "fingerprints" encoding structural information are the workhorse of cheminformatics and machine learning in drug discovery applications. However, fingerprint representations necessarily emphasize particular aspects of the molecular structure while ignoring others, rather than allowing the model to make data-d…
Physics-Constrained Predictive Molecular Latent Space Discovery with Graph Scattering Variational Autoencoderphysics.chem-ph
Model predicts stable molecules with AI and physics constraints.
problem Designing stable molecules with limited data.
method Graph Scattering Variational Autoencoder with physical constraints.
result Model generates stable molecules with desired properties.
Researchers use active subspaces to quantify uncertainty in deep generative models for molecular design.
problem Uncertainty quantification in deep generative models for molecular design due to high parameter space.
method Leveraging active subspaces to approximate posterior distribution over low-dimensional parameters.
result The proposed UQ scheme effectively estimates epistemic uncertainty in high-dimensional parameter space without altering model architecture.
Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamicsstat.ML
Timewarp accelerates molecular dynamics by learning to simulate long timescales.
problem Efficiently simulating long timescales in molecular dynamics.
method Uses a normalizing flow to learn large time steps in Markov chain Monte Carlo.
result Generalizes to unseen small peptides, accelerating sampling.
Molecule property prediction is a fundamental problem for computer-aided drug discovery and materials science. Quantum-chemical simulations such as density functional theory (DFT) have been widely used for calculating the molecule properties, however, because of the heavy computational cost, it is difficult to search a…
GAGA accelerates 3D molecular generation by replacing long trajectories with Gaussian approximations.
problem High computational cost of long generative trajectories in 3D molecular generation.
method GAGA identifies a characteristic step where molecular data becomes sufficiently Gaussian, replacing the trajectory with a Gaussian approximation.
result Significant improvement in both generation quality and computational efficiency.
Molecular structure-property relationships are key to molecular engineering for materials and drug discovery. The rise of deep learning offers a new viable solution to elucidate the structure-property relationships directly from chemical data. Here we show that the performance of graph convolutional networks (GCNs) for…
Graph Polish optimizes molecular structures by minimizing changes and maximizing preservation.
problem Error-prone traditional molecular optimization methods.
method Graph Polish transforms optimization into a polishing task, focusing on optimization centers and minimizing changes.
result Significant advantage over state-of-the-art methods on multiple optimization tasks.