GraphAF generates chemically valid molecules efficiently and accurately.
problem Generating chemically valid molecular structures while optimizing chemical properties.
method Flow-based autoregressive model combining autoregressive and flow-based approaches.
result GraphAF generates 68% chemically valid molecules without chemical knowledge rules and 100% with rules, achieving state-of-the-art performance.
Chemical databases store information in text representations, and the SMILES format is a universal standard used in many cheminformatics software. Encoded in each SMILES string is structural information that can be used to predict complex chemical properties. In this work, we develop SMILES2vec, a deep RNN that automat…
Neural networks learn molecule and material representations.
problem Learning efficient representations for molecules and materials.
method Continuous-filter convolutional network SchNet.
result SchNet accurately predicts chemical properties across various datasets.
Hyperbolic volume correlates with chemical properties of fullerenes.
problem Understanding the relationship between fullerene structure and chemical properties.
method Calculated hyperbolic volumes of fullerenes and correlated them with topological indices.
result Hyperbolic volume correlates with Wiener index and other topological indices of fullerenes.
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.
With access to large datasets, deep neural networks (DNN) have achieved human-level accuracy in image and speech recognition tasks. However, in chemistry, data is inherently small and fragmented. In this work, we develop an approach of using rule-based knowledge for training ChemNet, a transferable and generalizable de…
With the rise of deep neural networks for quantum chemistry applications, there is a pressing need for architectures that, beyond delivering accurate predictions of chemical properties, are readily interpretable by researchers. Here, we describe interpretation techniques for atomistic neural networks on the example of …
We propose a novel computational strategy for de novo design of molecules with desired properties termed ReLeaSE (Reinforcement Learning for Structural Evolution). Based on deep and reinforcement learning approaches, ReLeaSE integrates two deep neural networks - generative and predictive - that are trained separately b…
NLP techniques improve drug discovery by analyzing chemical and protein text.
problem Improving drug discovery through better analysis of chemical and protein text.
method Natural language processing techniques applied to biochemical entities.
result Enhanced prediction of molecular properties and design of novel molecules.
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.
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.
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.
Generating molecules with desired chemical properties is important for drug discovery. The use of generative neural networks is promising for this task. However, from visual inspection, it often appears that generated samples lack diversity. In this paper, we quantify this internal chemical diversity, and we raise the …
Graph neural network predicts protonation energies of oxygen atoms in bio-oil molecules.
problem Predicting protonation energies of oxygen atoms in bio-oil molecules for chemical upgrading.
method Site-specific graph neural network approach using iterative local nonlinear embedding.
result Effective prediction of protonation energies of individual oxygen atoms in bio-oil molecules.
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.
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.
Chemical autoencoders are attractive models as they combine chemical space navigation with possibilities for de-novo molecule generation in areas of interest. This enables them to produce focused chemical libraries around a single lead compound for employment early in a drug discovery project. Here it is shown that the…
Graph Convolutional Neural Networks identify molecular functional groups.
problem Automatic discovery of molecular functional groups to reduce lab experiments.
method Graph Convolutional Neural Networks (GCNNs) trained on relational graphs of molecules.
result Grad-CAM method identified the most specific and relevant molecular substructures.
New method predicts activity coefficients for binary mixtures without using physical descriptors.
problem Predicting activity coefficients for unexplored binary mixtures.
method Probabilistic matrix factorization model.
result Method outperforms state-of-the-art models requiring less training effort.
MatGAN uses GAN to efficiently generate new inorganic materials.
problem Efficiently searching the vast chemical design space for new materials.
method Generative adversarial network (GAN) trained on ICSD materials database.
result 92.53% novelty and 84.5% chemically valid samples generated.
New method finds graphene nanocrystals with reduced DFT calculations.
problem Efficiently discovering materials with desired properties in high-dimensional chemical space.
method Bayesian optimization with neural network kernel to minimize DFT calculations.
result Reduced computational cost by 20% for discovering materials with target properties.
Improved chemical predictions through compressed atomic species representations.
problem Intractable chemical space of molecules and materials.
method Introducing elemental modes for compressed representation of atomic species.
result Elemental modes enable improvements in machine learning tasks for chemical predictions.
Improved atomistic model predicts molecular properties using weighted skip-connections.
problem Understanding the relative importance of interactions in molecular property prediction.
method Extended SchNet architecture with weighted skip-connections to analyze molecule properties.
result Relative weighting of interaction blocks depends on molecule's chemical composition and configurational degrees of freedom.
Automates molecule design with simpler SMILES generation and reinforcement learning.
problem Designing molecules with specific chemical properties.
method Combines context-free grammar for SMILES strings and reinforcement learning with a Transformer model.
result Significantly reduces model steps per atom and beats previous baselines.
Rank-based Bayesian Optimization improves molecule selection in chemical systems.
problem Optimizing chemical compounds using traditional regression models.
method Introducing Rank-based Bayesian Optimization (RBO) using ranking models.
result RBO outperforms regression-based BO, especially for rough landscapes and activity cliffs.
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.
Framework separates chemical and structural contributions to aqueous solubility.
problem Merging chemical and structural information in solubility models obscures their relative importance.
method Additive MLP-GNN framework with separate chemical and structural branches.
result Framework reveals distinct roles of chemical and structural information in solubility.
A framework separates chemical and structural contributions to aqueous solubility.
problem Merging chemical and structural information in solubility models obscures their relative contributions.
method Additive MLP-GNN framework with separate chemical and structural branches.
result Framework reveals distinct roles of chemical and structural information in solubility.
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.
Stochastic fluctuations of molecule numbers are ubiquitous in biological systems. Important examples include gene expression and enzymatic processes in living cells. Such systems are typically modelled as chemical reaction networks whose dynamics are governed by the Chemical Master Equation. Despite its simple structur…
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 …
Fragmentation methods such as the many-body expansion (MBE) are a common strategy to model large systems by partitioning energies into a hierarchy of decreasingly significant contributions. The number of fragments required for chemical accuracy is still prohibitively expensive for ab-initio MBE to compete with force fi…
Deep generative models for graph-structured data offer a new angle on the problem of chemical synthesis: by optimizing differentiable models that directly generate molecular graphs, it is possible to side-step expensive search procedures in the discrete and vast space of chemical structures. We introduce MolGAN, an imp…
Machine learning recreates the periodic table from element properties.
problem Recreating the periodic table using machine learning.
method Unsupervised machine learning with GTM for feature embedding.
result PTG autonomously generates various periodic table layouts.
MHG-VAE achieves 100% valid molecules with simpler architecture.
problem Generating valid molecules and evaluating properties efficiently.
method MHG-VAE uses molecular hypergraph grammar to guide a single VAE.
result 100% validity achieved with simpler architecture.
Generative model learns to create molecules with multiple properties using interpretable substructures.
problem Creating molecules with multiple chemical properties is challenging.
method Compose molecules from substructures identified as responsible for each property, using graph generative models.
result Significant improvements in accuracy, diversity, and novelty of generated compounds over state-of-the-art baselines.
MoleculeSTM learns from molecule structures and texts for better drug design.
problem Lack of integration between chemical structures and textual knowledge in AI drug discovery.
method Jointly learns chemical structures and texts via contrastive learning, using a large dataset.
result MoleculeSTM achieves state-of-the-art performance in zero-shot tasks like structure-text retrieval and molecule editing.
The paper develops a Gaussian process model for predicting chemical efficacy.
problem Statistical methodologies for analyzing chemical databases are limited.
method Conditional Gaussian process models with Tanimoto distance and a scaling parameter.
result Predictive performance improves when accounting for chemical space correlation.
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.
Neural networks predict substructures from mass spectra to identify chemical threats.
problem Identifying unknown chemical threats from mass spectra and formulas.
method Data-driven approach using neural networks to rank and match substructures.
result Substructure classifiers achieve over 90% micro F1-score and correctly identify structures in 88-71% of cases.
Nuclear magnetic resonance (NMR) spectroscopy exploits the magnetic properties of atomic nuclei to discover the structure, reaction state and chemical environment of molecules. We propose a probabilistic generative model and inference procedures for NMR spectroscopy. Specifically, we use a weighted sum of trigonometric…
Generating novel graph structures that optimize given objectives while obeying some given underlying rules is fundamental for chemistry, biology and social science research. This is especially important in the task of molecular graph generation, whose goal is to discover novel molecules with desired properties such as …
This review discusses challenges and solutions for AI in chemical engineering.
problem Challenges in applying classical machine learning to chemical engineering data.
method Identifying four data characteristics and discussing their applications and solutions.
result Current research extends data science and machine learning to handle chemical engineering data challenges.
Proposes SGCN for spatially structured data.
problem Lack of node neighbor ordering in GCNs.
method Uses spatial features to learn from graphs with spatial positions.
result Empirically outperforms state-of-the-art methods.
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.
The meteoric rise of deep learning models in computer vision research, having achieved human-level accuracy in image recognition tasks is firm evidence of the impact of representation learning of deep neural networks. In the chemistry domain, recent advances have also led to the development of similar CNN models, such …
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.
Novel ML model predicts solvation free energies from atom interactions.
problem Predicting solvation free energies from atomistic interactions.
method Two encoding functions extract atomic feature vectors, interactions calculated by inner product.
result Outstanding performance and transferability on 6,493 experimental measurements.