MOSES benchmarks molecular generation models using a standardized dataset and metrics.
problem Unclear comparison and ranking of molecular generation models.
method Developed MOSES platform with training and testing datasets, metrics.
result Suggested MOSES results as reference for advancements in generative chemistry.
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.
Generates valid Euclidean distance matrices for molecular structures.
problem Generating point clouds in arbitrary rotations and translations is challenging.
method Developed a neural network architecture that produces valid Euclidean distance matrices invariant to rotations and translations.
result The architecture can generate molecular structures in a one-shot fashion by producing Euclidean distance matrices with a three-dimensional embedding.
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.
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…
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…
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.
Machine learning improves molecular dynamics simulations by reducing costs and enhancing accuracy.
problem Inaccurate and costly molecular dynamics simulations hinder chemical system description.
method Adaptive sampling of reference data points and machine learning models for predicting molecular properties.
result Machine learning models can predict molecular dipole moments and infrared spectra accurately.
Machine learning predicts molecular crystal stability.
problem Predicting the stability of molecular crystals.
method Supervised and unsupervised machine learning techniques to classify and predict lattice energy.
result Data-driven assessment of chemical groups' contribution to crystal stability.
Model learns molecular structures from graphs without explicit rules.
problem Learning molecular structures from graphs without explicit rules.
method Adapted Transformer model for undirected molecular graphs.
result Transformer model can learn complex molecular structures.
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.
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.
A^2-Net learns to estimate molecular structures from Cryo-EM data.
problem Estimating molecular structures from Cryo-EM density volumes.
method Learning-based approach using 3D detection and pose estimation.
result Achieves 91% coverage on new dataset and is hundreds of times faster.
New method uses cohomology to quantify molecular similarity.
problem Quantifying structural dissimilarity in molecular data.
method Gromov-Hausdorff ultrametric based on simplicial complexes and cohomology.
result Demonstrates effectiveness in clustering organic-inorganic halide perovskite structures.
Paper proposes a self-training method to generate molecular targets.
problem Challenges in training generative models for complex molecular design.
method Iterative target augmentation using a property predictor and EM iterations.
result Significant gains in molecular design, outperforming previous methods.
Generative models accelerate chemical design from properties to structures.
problem Expensive and incremental strategies for optimizing chemical properties.
method Review of current deep generative models and their application to molecular systems.
result Generative models can expedite the design of novel useful compounds.
There is an increasing demand for computing the relevant structures, equilibria and long-timescale kinetics of biomolecular processes, such as protein-drug binding, from high-throughput molecular dynamics simulations. Current methods employ transformation of simulated coordinates into structural features, dimension red…
MolHF generates complex molecules with hierarchical flow-based model.
problem Designing novel molecular structures with desired properties.
method MolHF is a hierarchical normalizing flow model that generates molecular graphs in a coarse-to-fine manner.
result MolHF achieves state-of-the-art performance in random generation and property optimization.
Deep neural network generates molecular structures close to equilibrium.
problem Discovery of atomistic systems with desirable properties.
method Autoregressive, convolutional deep neural network architecture.
result Model generates molecules close to equilibrium for C7O2H10 isomers.
GraphNVP generates molecular graphs efficiently and reversibly.
problem Generating valid molecular graphs with desired properties.
method Decomposes graph generation into adjacency tensor and node attributes, using reversible flows.
result Efficiently generates valid molecular graphs with minimal duplicates and latent space for property generation.
New algorithm improves model generalization in structured biomedical domains.
problem Improving model generalization in structured biomedical domains.
method Proposes a new regret minimization (RGM) algorithm and its structured extension for better performance in diverse environments.
result Significantly outperforms previous state-of-the-art baselines on molecular property prediction, protein homology, and stability prediction.
Selective prediction framework reduces errors in molecular structure identification from MS/MS.
problem High-stakes applications require reliable molecular structure identification from MS/MS data.
method Selective prediction framework using risk-coverage tradeoff and uncertainty quantification.
result First-order confidence measures and retrieval-level aleatoric uncertainty achieve strong risk-coverage tradeoffs.
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.
ASGN uses active semi-supervised learning to predict molecular properties efficiently.
problem Predicting molecular properties with scarce labeled data and high computational cost.
method ASGN combines a teacher-student framework with active learning to handle joint representation and property learning.
result ASGN achieves remarkable performance in property prediction on public datasets.
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.
New task aligns molecular structure with gene expression changes.
problem Modeling the relationship between chemical structure and gene expression changes.
method Developed a cross-modal small molecule retrieval task and a coordinated deep learning approach to align chemical structure and gene expression profiles.
result Demonstrated the feasibility of the new task and highlighted the limitations of current data and systems.
LaPool improves molecular graph representation learning by capturing interaction importance.
problem Lack of efficient intermediate pooling steps in GNNs leads to poor molecular substructure representation.
method LaPool is a novel, data-driven, and interpretable hierarchical graph pooling method that considers node features and graph structure.
result LaPool outperforms recent GNNs on molecular graph prediction and understanding tasks.
A major challenge in computational chemistry is the generation of novel molecular structures with desirable pharmacological and physiochemical properties. In this work, we investigate the potential use of autoencoder, a deep learning methodology, for de novo molecular design. Various generative autoencoders were used t…
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.
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.
Paper tackles multi-task learning for molecular property prediction with limited data.
problem Limited labeled data for each molecular property task in drug discovery.
method Proposes SGNN-EBM method to utilize relation graph between tasks and improve multi-task learning performance.
result Empirical results show the effectiveness of SGNN-EBM.
We seek to automate the design of molecules based on specific chemical properties. In computational terms, this task involves continuous embedding and generation of molecular graphs. Our primary contribution is the direct realization of molecular graphs, a task previously approached by generating linear SMILES strings …
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.
BBRT improves molecular properties through iterative translation.
problem Optimizing molecular structures for improved biochemical properties.
method Iterative translation of molecules using a black box approach.
result Improvement in molecular properties with each iteration of the translator.
PAGTN improves molecular property prediction by leveraging longer-range graph dependencies.
problem Local aggregation in GCNs misses higher-order graph properties.
method PAGTN uses path features and global attention layers to capture longer-range dependencies.
result PAGTN outperforms GCNs on various molecular property prediction datasets.
Automates GNN design for molecular property prediction.
problem Designing and tuning GNN architectures for molecular property prediction is labor-intensive.
method Developed a NAS approach to automatically discover high-performing GNN architectures for MPNNs.
result Automatically discovered MPNNs outperform manually designed GNNs in molecular property prediction.
Generative neural network designs novel 3D molecules with specified properties.
problem Designing molecules with desired properties in chemistry.
method Conditional generative neural network for 3D molecular structures.
result Demonstrated utility in generating novel molecules with specified motifs or composition.
MolecularRNN generates realistic molecules with desired properties.
problem Designing new molecules with specific properties.
method Graph recurrent generative model with likelihood pretraining and policy gradient tuning.
result Significant distribution shift to desired ranges for lipophilicity, drug-likeness, and melting point.
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.
HiGraphDTI learns drug and target representations from molecular graphs to predict DTIs.
problem Inaccurate drug-target interaction prediction due to insufficient chemical information extraction.
method Hierarchical graph representation learning to extract chemical information from atoms, motifs, and molecules.
result HiGraphDTI outperforms state-of-the-art methods in DTI prediction and interaction interpretation.
Tiered graph autoencoders improve molecular graph representation.
problem Representing and utilizing groups in molecular graphs.
method Adapting tiered graph autoencoders for PyTorch Geometric.
result Molecular graphs have tiered latent representations.
POEM predicts drug properties without tuning, outperforming other methods.
problem Predicting drug properties from molecular structures efficiently.
method POEM combines multiple molecular representations without hyperparameter tuning.
result POEM outperforms industry-standard methods across 17 tasks.
Unified machine learning predicts molecular wavefunctions efficiently.
problem Lack of explicit electronic structure in machine learning models for chemistry.
method Deep neural network for quantum mechanical wavefunction prediction.
result Efficient prediction of molecular wavefunctions with full electronic structure access.
Enhances solar cell efficiency prediction using deep neural networks.
problem Predicting HOMO values for organic solar cells from limited experimental data.
method Ensemble deep neural network (SINet) using SMILES and InChI molecular representations.
result Significant performance improvement from transfer learning and dual molecular representations.
Enhances drug discovery by optimizing molecular structures.
problem Accelerate drug discovery through better optimization of precursor molecules.
method Integrates substructure components with atom-level encoding in a fully autoregressive graph decoder.
result Significantly outperforms previous state-of-the-art baselines on molecular optimization tasks.
This paper proposes a new method to generate protein structures using deep learning.
problem Weak correlation between current scoring functions and protein molecular activity.
method Graph-generative models to sample novel tertiary protein structures.
result Generative models can reveal latent space and highlight structural factors.
Generative model creates new molecules retaining a scaffold with certainty.
problem Designing new molecules with a specific scaffold.
method Generative model that extends scaffold graph by adding vertices and edges.
result Model can generate novel molecules with high validity, uniqueness, and novelty.
Machine learning improves coarse-graining of molecular dynamics models.
problem Creating accurate coarse-grained models for molecular dynamics simulations.
method Reformulated coarse-graining as a supervised machine learning problem using statistical learning theory and deep learning (CGnets).
result CGnets can capture multi-body terms and all-atom explicit-solvent free energy surfaces with fewer coarse-grained beads.