A new method uses IVA to fuse diverse molecular features for better machine learning predictions.
problem Challenges in selecting features for accurate molecular property prediction.
method Independent Vector Analysis (IVA) for fusing multiple molecular feature vectors into a single, compact set.
result Improved prediction performance of regression models for molecular properties.
New molecular descriptors improve machine learning for small and large molecules.
problem Improving machine learning models for molecular properties.
method Developed constant-size molecular descriptors combining connectivity counts and encoded distances.
result Models using these descriptors perform comparably to or better than state-of-the-art models.
Automates feature selection and weighting in molecular systems.
problem Optimal feature selection and alignment in molecular systems.
method Differentiable Information Imbalance (DII) method for automated feature ranking and scaling.
result Automated feature selection and scaling that preserves information content and interpretability.
Improved molecular property prediction using attention and gate mechanisms.
problem Predicting molecular properties from chemical data.
method Attention and gate mechanisms in graph convolutional networks.
result Improved prediction of molecular properties, including photovoltaic efficiency.
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.
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.
A2I Transformer predicts atom energies from coordinates, avoiding heavy featurization.
problem Efficiently predicting atom energies from molecular coordinates with minimal featurization.
method End-to-end model using self-attention, permutation-equivariant.
result Stable predictions with significantly smaller errors than molecular dynamics simulations.
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.
Rotationally equivariant convolutions improve molecular property prediction.
problem Predicting molecular properties using graph neural networks.
method Ablation study with rotationally equivariant and invariant convolutions on QM9 data set.
result Rotationally equivariant layers decrease test error by an average of 23%.
MoleculeNet benchmarks molecular machine learning algorithms.
problem Lack of a standard benchmark for molecular machine learning.
method Curated multiple public datasets, established evaluation metrics, released open-source implementations.
result Learnable representations offer the best performance in molecular machine learning.
Optimizes feature selection for molecular kinetics models.
problem Selecting interpretable features for kinetic models from molecular dynamics simulations.
method Direct optimization of features without constructing a full kinetic model.
result Direct feature optimization leads to more efficient model selection.
Improves molecular activity prediction using graph convolutional neural networks considering graph distances.
problem Predicting molecular activity using graph convolutional neural networks with improved distance representation.
method Proposed three improvements: modified graph distances, distance-dependent weight matrices, and weighted sum conversion.
result The proposed method slightly outperforms the original weave module in compound activity prediction.
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.
Proposes a new model to predict polymer properties by integrating various data types.
problem Inaccurate polymer property prediction due to separate modeling of different data types.
method Multi-modal cascade feature transfer using GCN for chemical structure and molecular descriptors.
result Empirically evaluated model shows higher predictive performance than single-feature approaches.
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.
We introduce a convolutional neural network that operates directly on graphs. These networks allow end-to-end learning of prediction pipelines whose inputs are graphs of arbitrary size and shape. The architecture we present generalizes standard molecular feature extraction methods based on circular fingerprints. We sho…
New method pools graphs with edge features for molecular data.
problem Pooling graphs with edge features for molecular data.
method Proposes two types of pooling layers compatible with edge-feature graph-convolutional architecture.
result Significantly outperforms previous benchmarks on three out of four MoleculeNet datasets.
BoostMD accelerates molecular dynamics simulations by 8x with ML force fields.
problem Long inference times of ML force fields limit practical use in molecular dynamics.
method BoostMD uses previous time-step features to predict energies and forces, reducing complexity and computational cost.
result BoostMD achieves an 8-fold speedup and accurately samples the Boltzmann distribution.
Time-lagged autoencoders improve molecular dynamics data analysis.
problem Analyzing slow collective variables in molecular kinetics.
method Modified autoencoder neural network for dimension reduction.
result Time-lagged autoencoders reliably capture slow dynamics.
Deep featurization improves ADMET prediction accuracy.
problem Predicting ADMET properties to reduce clinical trial failures.
method Learning features from explicit molecular graphs using graph convolutions.
result Achieved unprecedented accuracy in ADMET property prediction.
Study predicts drug interactions and mutagenicity using subgraphs of molecular features.
problem Predicting interactions and mutagenicity in drug molecules.
method Used machine learning on subgraphs of molecular features to predict interactions and mutagenicity.
result Accurately predicted drug interactions and mutagenicity using subgraphs.
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.
3DGCN predicts molecular properties and biochemical activities using 3D molecular graph.
problem Predicting molecular properties and biochemical activities from 3D molecular graphs.
method Unified graph convolution with learning operations to handle spatial information, distinguishing 3D rotations.
result Significantly higher performance on various molecular tasks compared to other deep-learning models.
Machine learning generates coarse-grained force fields for molecular dynamics.
problem Creating thermodynamically consistent coarse-grained models for larger systems.
method Hybrid architecture using graph neural networks to learn molecular features.
result Framework reproduces thermodynamics for small biomolecular systems.
VAMPnets uses deep learning to model molecular kinetics from simulations.
problem Computing relevant molecular kinetics from simulations requires expert modeling.
method VAMPnets employs variational approach for Markov processes within neural networks.
result VAMPnets produces accurate kinetic models without requiring manual steps.
JAX MD enables differentiable physics simulations for molecular dynamics.
problem Performing efficient and differentiable physics simulations for molecular dynamics.
method Differentiable physics simulation environments, interaction potentials, neural networks, flexible primitives.
result Differentiable physics simulations can be used for meta-optimization and scaling to large particle systems.
Deep IDA integrates multi-view data to classify COVID-19 severity, identifying molecular signatures.
problem Understanding the complexity of COVID-19 severity from multi-view clinical and molecular data.
method Deep IDA learns nonlinear projections to maximize view associations and class separations, with feature ranking.
result Deep IDA outperforms other methods in classifying COVID-19 severity and identifies interpretable molecular signatures.
Cormorant learns molecular properties via rotationally covariant neural networks.
problem Learning molecular potential energy surfaces and properties.
method Rotationally covariant neural network architecture with tensor products and Clebsch-Gordan decomposition.
result Significantly outperforms competing algorithms in learning molecular Potential Energy Surfaces.
A new unpooling layer enhances graph generation in molecular models.
problem Efficient graph generation for complex models like molecules.
method Trainable unpooling layer that enlarges and restructures graphs.
result The unpooling layer improves graph generation in molecular models.
Improved molecular property prediction using WL embedding in GNNs.
problem Limited performance of GNNs in predicting molecular properties.
method Explored Weisfeiler-Lehman (WL) embedding to replace GNN layers, enhancing representability and performance.
result WL embedding consistently improves GNN performance across multiple datasets.
Dimensionality reduction helps analyze molecular simulations data.
problem High-dimensional molecular simulation data is hard to analyze.
method Various dimensionality reduction methods (k-means, autoencoder, PCA, tICA) applied to molecular simulation data.
result Methods learned different conformations of molecular processes.
Enhances graph neural networks with random walks to improve performance.
problem Limited input to graph neural networks, especially for molecular data.
method Random walk data processing to enrich graph neural network input.
result Shallow network outperforms deep GNNs using only node features.
Study compares atom representations in graph neural networks for molecular properties.
problem Incorrect attribution of results in molecular property prediction due to varying atom features.
method Evaluated multiple atom representations on free energy, solubility, and metabolic stability predictions.
result Different atom representations can lead to varying predictive performance in graph neural networks.
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.
Transformer-M learns molecular data in 2D or 3D formats.
problem Learning models for molecules are limited to specific data formats.
method Developed a Transformer-based model that can handle 2D and 3D molecular data.
result Transformer-M achieves strong performance on both 2D and 3D molecular tasks.
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.
GNN-FiLM uses feature-wise linear modulation to improve graph neural networks.
problem Improving graph neural networks for better performance.
method Feature-wise linear modulation applied to target node representations in GNNs.
result GNN-FiLM outperforms baseline methods on a regression task for molecular graphs.
IGNN improves GNNs by maximizing edge-state transform mutual information.
problem Optimizing GNNs for better relational information.
method Variational information maximization to learn optimal transform parameters.
result IGNN achieves state-of-the-art performance on molecular graph tasks.
AMPL is a new software pipeline for drug discovery models.
problem Reproducibility and traceability in drug discovery models.
method Modular and extensible software pipeline for machine learning models.
result Physicochemical descriptors and deep learning outperform traditional fingerprints.
New method uses space-filling curves to represent crystal structures for machine learning.
problem Representing molecular crystals for machine learning.
method SFC-M feature representations based on Morton curves, reduced by LSI.
result Promising results in predicting crystal properties.
EAGCN learns attention weights and node features for multi-relational graphs.
problem Learning molecular properties from complex graph structures.
method Edge attention-based multi-relational GCN (EAGCN) that learns attention weights and node features.
result EAGCN predicts compound properties from molecular graphs efficiently and interprets attention weights.
GMVAE improves clustering in molecular simulations data.
problem Clustering metastable states in multi-basin free-energy landscapes.
method Gaussian mixture variational autoencoder (GMVAE) for dimensionality reduction and clustering.
result Enhanced clustering of metastable states compared to standard VAEs.
Improving in-context learning for latent space Bayesian optimization by adapting pretraining on molecular latent space.
problem Improving in-context learning for latent space Bayesian optimization.
method Adapting pretraining on molecular latent space.
result Achieving strong performance on held-out molecular optimization benchmarks.
Algorithm uncovers latent attribute graph from molecular data.
problem Learning latent representations and interpreting them for limited data.
method Perturbation experiments on latent codes of a generative autoencoder.
result Effective graphical model of latent codes and attributes.
OmicsMapNet converts omics data into 2D images for deep learning analysis.
problem Analyzing high-dimensional omics data for phenotype classification.
method Reorganize omics data into 2D images, apply deep learning to classify, identify key features.
result Deep learning models accurately classify TCGA glioma samples based on molecular features.
A new method learns graph-level features for drug properties predication.
problem Predicting drug efficacy and toxicity from molecular graphs.
method Introducing a dummy super node connected to all nodes and modifying graph operations to learn graph-level features.
result The method improves molecular properties predication performance on MoleculeNet.
Paper proposes a method for semi-supervised learning of molecular representations.
problem Learning representations of molecules in a semi-supervised manner.
method Unsupervised hierarchical feature extraction algorithm using neural message passing.
result Method outperforms existing methods in benchmark datasets.
Gaussian process regression loses locality in high dimensions, affecting molecular energy surface fitting.
problem Loss of locality in high-dimensional Gaussian process regression.
method Analysis of Matern family kernels and multi-zeta basis functions.
result The property of locality disappears in high dimensions, impacting regression quality.