Bayesian neural networks quantify uncertainties in molecular property predictions.
problem Poor predictions in molecular property predictions due to unreliable training data.
method Bayesian neural networks to estimate model-driven and data-driven uncertainties.
result Uncertainty quantification is necessary for reliable molecular applications.
Reliable identification of molecular biomarkers is essential for accurate patient stratification. While state-of-the-art machine learning approaches for sample classification continue to push boundaries in terms of performance, most of these methods are not able to integrate different data types and lack generalization…
Bayesian learning improves reliability of molecular predictions for hit compound discovery.
problem Improving reliability of machine learning predictions for virtual screening.
method Bayesian learning algorithms applied to graph neural networks.
result Bayesian learning leads to well-calibrated predictions and higher hit compound success.
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.
Study evaluates uncertainty quantification methods for molecular property prediction.
problem Uncertainty in neural models for molecular property prediction.
method Systematically evaluated several UQ methods on five benchmark datasets.
result No single method is unequivocally superior, and none provides reliable error ranking across datasets.
Quantitative structure-activity relationship (QSAR) modelling is effective 'bridge' to search the reliable relationship related bioactivity to molecular structure. A QSAR classification model contains a lager number of redundant, noisy and irrelevant descriptors. To address this problem, various of methods have been pr…
Inspired by the success of deep learning techniques in the physical and chemical sciences, we apply a modification of an autoencoder type deep neural network to the task of dimension reduction of molecular dynamics data. We can show that our time-lagged autoencoder reliably finds low-dimensional embeddings for high-dim…
CGD improves diffusion models' out-of-distribution generalization.
problem Reliable sampling from high-value regions beyond training data.
method Context-guided diffusion (CGD) using unlabeled data and smoothness constraints.
result Substantial performance gains across various diffusion processes.
New method calibrates uncertainty in molecular property predictions.
problem Uncalibrated uncertainty estimates in molecular property predictions.
method Message Passing Neural Networks with calibrated probabilistic predictive distribution.
result Accurate molecular formation energy predictions with well-calibrated uncertainty.
This paper evaluates scalable uncertainty estimation methods for DNN-based molecular property prediction.
problem Uncertainty quantification in DNN models for molecular property prediction.
method Quantitative comparison of MC-Dropout, deep ensembles, and bootstrapping on the QM9 dataset.
result Ensembling and bootstrapping consistently outperform MC-Dropout, with different context-specific pros and cons.
MoReL models multi-omics data to find hidden molecular interactions.
problem Heterogeneous multi-omics data with varying quality and structure.
method Fused Gromov-Wasserstein (FGW) regularization in a deep Bayesian generative model.
result Enhanced performance in inferring meaningful interactions from real-world datasets.
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.
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.
This paper reviews deep learning and knowledge-based methods for molecular design.
problem Optimizing molecular properties for scientific advances and process performance.
method Survey of deep learning and knowledge-based methods for molecular design.
result Deep learning models show promise in overcoming computational challenges.
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…
Boosts GNN performance on molecular graphs.
problem Current GNNs struggle with training set and scalability.
method Proposes an auxiliary module to enhance GNNs.
result Improves GNN performance on molecular datasets.
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.
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.
Study improves reliability of neural models for virtual screening.
problem Reliability issues in neural models for molecular property prediction.
method Investigated model architectures, regularization, and loss functions.
result Correct choice of regularization and inference methods improves reliability.
Bayesian neural networks quantify uncertainty in molecular property predictions.
problem Uncertainty in molecular property predictions due to limited data quality and quantity.
method Bayesian neural networks to decompose and quantify model- and data-driven uncertainties.
result Data noise significantly affects data-driven uncertainties in molecular property predictions.
MuML models predict molecular dipole moments using atomic partial charges and dipoles.
problem Predicting molecular dipole moments accurately and efficiently.
method Combining atomic partial charges and atomic dipoles within a physically inspired ML model.
result MuML models achieve excellent transferability and accuracy, approaching DFT results at a fraction of the computational cost.
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.
SRV learns slow molecular modes from simulations.
problem Discovering slow collective motions in molecular dynamics.
method State-free reversible VAMPnets (SRV) for nonlinear CV approximation.
result SRVs capture slow dynamics in complex systems.
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.
Deep Bayesian neural networks improve somatic variant calling accuracy.
problem Improving accuracy in pinpointing somatic variants from next-gen sequencing data.
method Deep Bayesian Recurrent Neural Networks (RNNs) for somatic variant calling.
result Deep Bayesian RNNs provide more reliable confidence intervals for variant calls.
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.
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…
ChemBO optimizes small organic molecules for synthesis and desired properties.
problem Designing and optimizing new organic molecules for specific properties.
method Bayesian optimization framework that considers synthesizability constraints.
result ChemBO generates synthesizable candidates efficiently and effectively.
Develops active learning for scale-bridging simulations.
problem Quantitative predictions in nanoporous media and inertial confinement fusion.
method Active learning approach to optimize fine-scale simulations for coarse-scale hydrodynamics.
result Optimizes use of fine-scale simulations for coarse-scale predictions.
New molecular design model outperforms existing methods.
problem Designing valid, unique, and novel molecules.
method Adversarially Regularized Autoencoder (ARAE) combining latent variables from VAE and adversarial training from GAN.
result ARAE outperforms conventional models in validity, uniqueness, and novelty.
New method uses interval-based metric to validate prediction uncertainty in machine learning.
problem Validation of prediction uncertainty in machine learning regression tasks is unreliable due to heavy-tailed distributions.
method Shift from variance-based metrics to interval-based Prediction Interval Coverage Probability (PICP).
result PICP method more quickly and reliably tests prediction intervals than variance-based metrics.
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.
Advanced GNNs improve molecular generation models.
problem Generating complete graphs with multiple nodes and edges based on labels.
method Replaced standard GNNs with more expressive GNNs in autoregressive and one-shot generation models.
result Advanced GNNs can improve performance of graph generative models, but expressiveness is not a necessity.
This work improves molecular design by efficiently selecting diverse candidate molecules.
problem Designing molecules that satisfy multiple conflicting objectives.
method A modular 'generate-then-optimize' framework using generative models and a novel acquisition function.
result Significant improvements in sample efficiency across synthetic and application-driven tasks.
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.
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.
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.
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.
Molecular optimization aims to discover novel molecules with desirable properties. Two fundamental challenges are: (i) it is not trivial to generate valid molecules in a controllable way due to hard chemical constraints such as the valency conditions, and (ii) it is often costly to evaluate a property of a novel molecu…
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…
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.
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.
Framework learns physics-informed continuum models from molecular data.
problem Discovering accurate and robust data-driven continuum models from molecular simulation data.
method Operator regression framework using neural networks in modal space with physical inductive biases.
result Learned operators generalize to unseen system characteristics.
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.
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.
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.
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.