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.
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.
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.
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.
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.
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.
Enhances molecular design models by fine-tuning uncertainty-guided VAEs.
problem Fine-tuning pre-trained generative models for specific molecular property optimization.
method Uncertainty-guided fine-tuning of variational autoencoders in an active learning setting.
result Uncertainty-guided fine-tuning improves model performance across multiple molecular properties.
Active-GRPO improves molecular optimization by actively deciding when to imitate or self-improve.
problem Training robust and efficient molecular optimization models with large language models.
method Active-GRPO combines imitation and reinforcement learning, upgrading references and policies dynamically.
result Improves molecular optimization performance, achieving statistically significant gains.
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.
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.
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…
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.
Molecular dynamics simulations are an important tool for describing the evolution of a chemical system with time. However, these simulations are inherently held back either by the prohibitive cost of accurate electronic structure theory computations or the limited accuracy of classical empirical force fields. Machine l…
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.
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.
Improved neural network models predict molecular and material properties efficiently.
problem Training neural networks for accurate interatomic potentials is computationally expensive.
method Gaussian moment-based neural networks with improved architecture and active learning.
result The new models achieve high accuracy and reduced training times.
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.
Signaling proteins are an important topic in drug development due to the increased importance of finding fast, accurate and cheap methods to evaluate new molecular targets involved in specific diseases. The complexity of the protein structure hinders the direct association of the signaling activity with the molecular s…
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.
Two new minor minimal intrinsically chiral graphs identified.
problem Identifying intrinsically chiral graphs in molecular structures.
method Analyzing graph symmetry and embedding properties.
result Found two new minor minimal intrinsically chiral graphs Γ7 and Γ8. New method uses biased MD to create accurate MLIPs.
problem Creating a comprehensive data set for MLIPs.
method Bias MD by MLIP's energy uncertainty, using gradient-based uncertainties.
result Develops uniformly accurate MLIPs with lower computational cost.
Functional groups (FGs) are molecular substructures that are served as a foundation for analyzing and predicting chemical properties of molecules. Automatic discovery of FGs will impact various fields of research, including medicinal chemistry and material sciences, by reducing the amount of lab experiments required fo…
Timely assessment of compound toxicity is one of the biggest challenges facing the pharmaceutical industry today. A significant proportion of compounds identified as potential leads are ultimately discarded due to the toxicity they induce. In this paper, we propose a novel machine learning approach for the prediction o…
A new RL framework optimizes drug-like molecules synthetically.
problem Optimizing drug-like molecules for specific criteria.
method Deep Reinforcement Learning framework for chemical space optimization.
result Outperforms existing methods in pharmacological optimization.
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.
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.
We present a three-dimensional graph convolutional network (3DGCN), which predicts molecular properties and biochemical activities, based on 3D molecular graph. In the 3DGCN, graph convolution is unified with learning operations on the vector to handle the spatial information from molecular topology. The 3DGCN model ex…
Deep neural networks have outperformed existing machine learning models in various molecular applications. In practical applications, it is still difficult to make confident decisions because of the uncertainty in predictions arisen from insufficient quality and quantity of training data. Here, we show that Bayesian ne…
A faster method for optimizing DNA and protein sequences using machine learning.
problem Designing DNA and protein sequences with improved function.
method Activation maximization with a straight-through approximation and adaptive entropy variable.
result Fast SeqProp achieves up to 100-fold faster convergence and improved fitness optima.
QSAR models struggle to predict activity cliffs, but graph isomorphism features improve AC-sensitivity.
problem QSAR models struggle to predict activity cliffs (ACs).
method Nine distinct QSAR models combining molecular representation methods and regression techniques.
result Graph isomorphism features improve AC-sensitivity.
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…
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.
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.
Deep learning model integrates SMILES and molecular descriptors for EGFR inhibitor prediction.
problem Improving drug discovery by integrating structural and property data.
method Attention-based deep learning architecture trained on SMILES and molecular descriptors.
result Max MCC 0.58 and AUC 90% on EGFR inhibitors dataset, outperforming reference model.
Predicting bioactivity and physical properties of small molecules is a central challenge in drug discovery. Deep learning is becoming the method of choice but studies to date focus on mean accuracy as the main metric. However, to replace costly and mission-critical experiments by models, a high mean accuracy is not eno…
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.
Semi-supervised learning improves QSAR model predictions for novel compounds.
problem Improving model predictions for compounds not in the training set and adjusting for selection bias.
method Semi-supervised learning framework to estimate model quality and adjust for selection bias.
result Predictions for novel compounds are improved by accounting for compound similarity and selection bias.
In this work, we present an application of Locally Interpretable Machine-Agnostic Explanations to 2-D chemical structures. Using this framework we are able to provide a structural interpretation for an existing black-box model for classifying biologically produced fuel compounds with regard to Research Octane Number. T…
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.
Molecular Dynamics (MD) simulation is widely used to analyze the properties of molecules and materials. Most practical applications, such as comparison with experimental measurements, designing drug molecules, or optimizing materials, rely on statistical quantities, which may be prohibitively expensive to compute from …
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.
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…
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…
Genomics has revolutionized biology, enabling the interrogation of whole transcriptomes, genome-wide binding sites for proteins, and many other molecular processes. However, individual genomic assays measure elements that interact in vivo as components of larger molecular machines. Understanding how these high-order in…
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.
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.
Generative model learns molecular geometry from graph representations.
problem Generating equilibrium states for molecular systems is computationally expensive.
method Probabilistic model based on Euclidean distance geometry.
result Generative model achieves state-of-the-art accuracy in molecular conformation generation.
Improved RL model for fragment-based molecule generation.
problem Generating molecules with high docking scores.
method Thorough reproduction, scrutiny, and improvement of the FREED model.
result The improved model produces molecules with superior docking scores.