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.
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 aids excited-state molecular dynamics studies.
problem Challenges in studying electronically excited states of molecules.
method Employing machine learning techniques for excited-state molecular dynamics.
result Highlight successes and challenges in machine learning for excited-state processes.
Machine learning speeds up molecular photodynamics simulations to nanosecond scales.
problem High cost of quantum chemistry limits accurate long time scale simulations.
method Use machine learning to predict electronic properties from molecular geometry.
result Machine learning algorithms can simulate photodynamics with higher efficiency and accuracy.
Quantum machine learning boosts drug discovery efficiency.
problem Enhancing drug discovery through quantum computing.
method Quantum neural networks on gate-based quantum computers.
result Significant advancements in molecular property prediction and generation.
New machine learning method for analyzing molecular conformation dynamics.
problem Understanding conformation dynamics of biomolecules.
method Combines kernel-based techniques with transfer operator theory.
result Many prominent methods are special cases of this approach.
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.
A dataset of 10 molecule types for machine learning studies.
problem Lack of suitable datasets for machine learning in molecular imaging.
method Generated 2D cross-sectional projections of 10 molecule types from Molecular Dynamics trajectories.
result Benchmark dataset for machine learning, deep learning, and image processing in scattering, imaging, and microscopy.
AI models predict new opioid ligands from molecular dynamics.
problem Lack of crystal structures limits virtual screening of drug candidates.
method Molecular dynamics simulation and machine learning.
result Identified a novel μ opioid chemotype. This paper explores machine learning landscapes using molecular energy analogy.
problem Understanding the solution space and nature of predictions in machine learning.
method Analogy with molecular potential energy landscapes to explore machine learning landscapes.
result Emergent properties of machine learning landscapes can be related to molecular structure, thermodynamics, and kinetics.
New methods use machine learning to simulate rare transitions in molecular systems.
problem Simulating rare transitions between metastable states in molecular dynamics.
method Generative models and reinforcement learning for importance sampling.
result Efficiently generated transition paths linking metastable states.
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.
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.
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.
DenSNet learns electron densities for molecular dynamics, enabling accurate spectroscopic predictions.
problem Lack of accurate electronic observables in MLIPs for molecular dynamics.
method DenSNet uses SE(3)-equivariant neural networks to predict electron densities and total energy.
result DenSNet predicts infrared spectra with excellent agreement to experimental data.
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…
Machine learning speeds up infrared spectra prediction for large molecules.
problem Accurate prediction of molecular infrared spectra with high efficiency.
method Ab initio molecular dynamics simulations combined with neural network models.
result Highly accurate infrared spectra predictions for large molecules.
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.
SchNetPack 2.0 enhances atomistic machine learning with improved neural networks.
problem Improving atomistic machine learning methods and applications.
method Improved data pipeline, equivariant neural networks, PyTorch implementation, PyTorch Lightning, Hydra configuration framework.
result Easy extension and complex training tasks support.
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.
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.
GRAPE uses graph kernels to predict molecular energies efficiently.
problem Efficiently predicting molecular energies with physical constraints.
method GRAPE approach based on graph theory incorporating symmetries.
result GRAPE predicts atomization energies accurately on organic molecules.
A machine learning model captures non-Newtonian fluid dynamics from molecular details.
problem Creating accurate non-Newtonian fluid models from molecular data.
method Developed a machine learning framework that maps micro-scale polymer configurations to macro-scale fluid dynamics, preserving molecular fidelity.
result The deep non-Newtonian model (DeePN2) accurately predicts fluid behavior without empirical closures. Machine learning is used to approximate the kinetic energy of one dimensional diatomics as a functional of the electron density. The functional can accurately dissociate a diatomic, and can be systematically improved with training. Highly accurate self-consistent densities and molecular forces are found, indicating the…
PIMKL uses pathway knowledge to improve sample classification and provide interpretable molecular signatures.
problem Lack of reliable molecular biomarkers and poor generalization in machine learning for health care.
method Pathway Induced Multiple Kernel Learning (PIMKL) using a mixture of pathway-induced kernels optimized via Multiple Kernel Learning.
result PIMKL provides interpretable molecular signatures and stable predictions.
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.
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.
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.
Solves the initial CV problem for molecular simulations using machine learning.
problem Selecting appropriate collective variables for enhancing sampling in molecular simulations.
method Data-driven approach inspired by supervised machine learning (SML).
result Various SML algorithms can be used as initial collective variables (SML_cv) for accelerated sampling.
Machine learning improves long-time molecular dynamics simulations.
problem Expensive long-time MD simulations for practical applications.
method Novel machine learning techniques for efficient sampling and model learning.
result Machine learning can revolutionize long-timescale MD simulations.
We introduce a machine learning model to predict atomization energies of a diverse set of organic molecules, based on nuclear charges and atomic positions only. The problem of solving the molecular Schrödinger equation is mapped onto a non-linear statistical regression problem of reduced complexity. Regression models a…
DeepGG generates graph distributions for drug discovery and molecular design.
problem Learning graph distributions for various applications.
method Improved deep graph generator based on deep state machines with graph and node embeddings.
result The state machine design favors specific graph distributions.
AI helps in drug discovery with understandable explanations.
problem Understanding the complex models behind AI-generated drugs.
method Explainable AI methods to interpret deep learning models.
result Improved interpretability of AI-generated drug properties.
Machine learning models accurately predict molecular magnetic anisotropy tensors.
problem Accurately modeling molecular magnetic anisotropy tensors.
method Gaussian-moment neural-network approach for machine learning.
result Achieved accuracy of 0.3--0.4 cm−1 for magnetic anisotropy tensor predictions. This review explores the use of machine learning in discovering collective variables for biomolecular dynamics.
problem Understanding the conformational dynamics and molecular recognition in biomolecules.
method Statistical analysis of high-dimensional spatiotemporal data generated from molecular dynamics simulations.
result Machine learning algorithms can be used to discover abstract collective variables that describe biomolecular dynamics.
wACSFs improve machine learning potentials for molecular structures.
problem Improving accuracy and efficiency of machine learning potentials for molecular structures.
method Introducing wACSFs based on ACSFs, optimizing parameters with a genetic algorithm.
result wACSFs lead to better generalization performance and require fewer descriptors.
Alchemy dataset benchmarks AI models in chemistry.
problem Lack of diverse molecular datasets for AI models in chemistry.
method Developed a new molecular dataset with 119,487 organic molecules and benchmarked AI models on it.
result Demonstrated the usefulness of new data in validating and developing machine learning models for chemistry.
MD-GAN learns long-time molecular behavior from short-time data with multi-particle input.
problem Accurately predicting long-time molecular dynamics from short-time data.
method Machine learning method (MD-GAN) that incorporates dynamics of multiple particles of molecules.
result Predicting diffusion with one-third of the training data length using multi-particle input.
New method combines deep learning and quantum mechanics for efficient molecular statistics.
problem Computational expense in extracting statistics from molecular systems.
method Adaptive Markov chain Monte Carlo with Normalizing Flow and MLP for quantum accuracy.
result Rapid convergence to Boltzmann distribution and accurate thermodynamic observables.
Machine learning bypasses Kohn-Sham equations for faster DFT calculations.
problem Solving the Kohn-Sham equations for electronic structure problems.
method Directly learning density-potential and energy-density maps for test systems and molecules.
result Improved accuracy and lower computational cost demonstrated for molecular geometries.
Method clusters molecular systems based on dynamics or structure similarity.
problem Clustering molecular systems based on dynamics or structure similarity.
method Ward's minimum variance clustering using Jensen-Shannon divergence.
result Method avoids overfitting in supervised learning.
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.
Graph neural networks improve molecular property prediction.
problem Efficiently predicting molecular properties with high accuracy and scalability.
method Gated Graph Recursive Neural Networks (GGNN) with skip connections.
result GGNN achieves state-of-the-art performance on molecular property prediction benchmarks.
SMILES Transformer learns molecular fingerprints for drug discovery.
problem Poor performance of rule-based molecular fingerprints in shallow prediction models or small datasets.
method Unsupervised pre-training of a sequence-to-sequence language model on a corpus of SMILES.
result SMILES Transformer outperformed existing methods in small-data settings.
Machine learning predicts signaling peptides from protein star graphs.
problem Predicting signaling activity of proteins from molecular structure.
method Protein star graphs, S2SNet topological indices, Machine Learning (SVM-RFE, Laplacian kernel).
result Best model predicts 98.0% signaling pathways with AUROC 0.961.
A Graph Neural Network model for generating molecular graphs.
problem Designing new drug molecules efficiently and cost-effectively.
method Sequential molecular graph generator based on Graph Neural Networks.
result The model can generate molecular graphs without overfitting and outperforms existing methods.
Graph neural networks improve odor prediction from molecular structure.
problem Predicting odor from molecular structure is challenging and important.
method Used graph neural networks for QSOR modeling.
result Graph neural networks significantly outperform prior methods on a novel data set.
Method learns molecular Hamiltonian for accurate electron dynamics predictions.
problem Predict electron dynamics in molecules using learned Hamiltonians.
method Combines linear statistical model with quantum Liouville equation time discretization.
result Predicted electron dynamics closely matches ground truth, even beyond training data.