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.
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…
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.
A major challenge in materials design is how to efficiently search the vast chemical design space to find the materials with desired properties. One effective strategy is to develop sampling algorithms that can exploit both explicit chemical knowledge and implicit composition rules embodied in the large materials datab…
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.
MEGAN models chemical reactions as graph edits, improving synthesis planning.
problem Generating and predicting chemical reactions under constraints.
method End-to-end encoder-decoder neural model inspired by arrow pushing formalism.
result State-of-the-art accuracy in standard benchmarks for retrosynthesis prediction.
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 …
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.
Deep Learning has been shown to learn efficient representations for structured data such as image, text or audio. In this chapter, we present neural network architectures that are able to learn efficient representations of molecules and materials. In particular, the continuous-filter convolutional network SchNet accura…
Local PCA detects intrinsic parameterization of complex thermo-chemical state-spaces.
problem Detecting intrinsic parameterization of complex thermo-chemical state-spaces.
method Local PCA applied to local clusters of data.
result Local PCA finds meaningful parameterization linked to local stoichiometry, reaction progress, and soot formation processes.
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.
Bio-oil molecule assessment is essential for the sustainable development of chemicals and transportation fuels. These oxygenated molecules have adequate carbon, hydrogen, and oxygen atoms that can be used for developing new value-added molecules (chemicals or transportation fuels). One motivation for our study stems fr…
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…
A new drug embedding method using hierarchical drug relations and chemical structures.
problem Learning accurate drug representations from chemical structures and hierarchies.
method Semi-supervised drug embedding using VAE in hyperbolic space.
result The method accurately places drugs in a hierarchy and predicts side-effects.
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.
AI learns to design chemical processes efficiently.
problem Designing efficient chemical processes.
method Hierarchical reinforcement learning and graph neural networks.
result Quick learning in various decision spaces.
Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fast and accurate predictions of atomistic chemical properties, they do not explicitly capture the electronic degrees of freedom of a molecule…
Chemical structure elucidation is a serious bottleneck in analytical chemistry today. We address the problem of identifying an unknown chemical threat given its mass spectrum and its chemical formula, a task which might take well trained chemists several days to complete. Given a chemical formula, there could be over a…
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.
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.
Generates natural product-like compounds using GPT models.
problem Challenges in generating and evaluating natural product-like compounds.
method Trained GPT-based chemical language models on natural product dataset.
result Generated compounds have similar distribution to natural products.
Analyzes Indian chemical industry post-Covid.
problem Global uncertainty impacts chemical industry performance.
method Fundamental analysis of key players and trends.
result Various geopolitical and macroeconomic trends shape industry performance.
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…
CRNN discovers chemical reaction pathways from data.
problem Challenging to infer reaction pathways for complex systems.
method Neural network approach that satisfies fundamental physics laws.
result CRNN autonomously discovers reaction pathways from species concentration data.
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…
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.
Paper explores ML for UV spectra, showing transferability in chemical space.
problem Modeling excited states and predicting properties of unseen molecules.
method Adapting charge model for excited states, using SchNarc approach.
result ML models can predict properties of unseen molecules and different excited states.
We use the formalism of Geometrothermodynamics to describe chemical reactions in the context of equilibrium thermodynamics. Any chemical reaction in a closed system is shown to be described by a geodesic in a 2−dimensional manifold that can be interpreted as the equilibrium space of the reaction. We first show this i…
Chemical networks outperform spiking neural networks in classification tasks.
problem Learning tasks with spiking neural networks require hidden layers, which are computationally expensive.
method Used deterministic mass-action kinetics to prove chemical reaction networks without hidden layers can solve tasks previously solved by spiking neural networks.
result A chemical reaction network without hidden layers outperforms a spiking neural network with hidden layers in a handwritten digit classification task.
Geometric approach to thermodynamics of chemical reaction networks.
problem Thermodynamics of chemical reaction networks with non-ideal behavior.
method Information geometry, Riemannian geometry, Cramer-Rao bound, absolute sensitivity.
result Absolute sensitivity is a projection operator onto the tangent bundle of the equilibrium manifold.
Researchers derive the chemical potential equation for ideal agent systems.
problem Missing equation of state for chemical potential in ideal agent systems.
method Derived from econophysical model assumptions of ideal agent systems.
result Equation of state for chemical potential derived for ideal agent systems.
Chemical transport models (CTMs), which simulate air pollution transport, transformation, and removal, are computationally expensive, largely because of the computational intensity of the chemical mechanisms: systems of coupled differential equations representing atmospheric chemistry. Here we investigate the potential…
CSLVAE generates large chemical libraries efficiently.
problem Navigating ultra-large combinatorial synthesis libraries.
method Hierarchically-organized database with molecular encoder and decoder.
result Generates valid molecular graphs without autoregression.
DeepSIBA predicts biological effects of chemical structures using graph neural networks.
problem Predicting biological effects of chemical structures for drug discovery.
method Siamese Graph Convolutional Neural Networks for structure-biological effect mapping.
result Highly accurate predictions of biological effects for structurally dissimilar compounds.
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…
CRPS improves GP-based sequential design for chemical space.
problem Finding molecules with specific properties in synthetic chemistry.
method Threshold-weighted CRPS as acquisition function for GP models in sequential design.
result Improved performance in molecule research with CRPS-based strategies.
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 …
During reactive transport modeling, the computational cost associated with chemical reaction calculations is often 10-100 times higher than that of transport calculations. Most of these costs results from chemical equilibrium calculations that are performed at least once in every mesh cell and at every time step of the…
A new method uses active learning to improve chemical simulation efficiency.
problem Efficiently estimating equilibrium-based chemical simulations.
method Sequential data-driven approach using Gaussian process uncertainty.
result Significantly reduced number of function evaluations.
Q-SAVI model improves drug discovery accuracy with prior knowledge of chemical space.
problem Challenges in drug discovery due to covariate shift and limited labeled data.
method Probabilistic model with domain-informed prior distributions over functions.
result Q-SAVI outperforms state-of-the-art techniques in predictive accuracy and calibration.
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.
ChemGrapher uses deep learning to automatically convert chemical compound images into accurate graphs.
problem Automatically converting chemical compound images into accurate graphs with correct bond multiplicity and stereochemical information.
method Developed a deep neural network model for optical compound recognition, including segmentation and classification models.
result Significant error reductions in bond multiplicity and stereochemical information compared to existing tools.
Geometric modeling for human food and chemical sensitivities.
problem Modeling biochemical processes in humans with sensitivities.
method Geometric approach to biochemical modeling.
result Geometric models improve understanding of sensitivities.
Chemical plants are complex and dynamical systems consisting of many components for manipulation and sensing, whose state transitions depend on various factors such as time, disturbance, and operation procedures. For the purpose of supporting human operators of chemical plants, we are developing an AI system that can s…
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…
Upper bound on CRN reaction rates derived using information geometry.
problem Challenging task of deriving an upper bound on reaction rates of nonlinear, discrete CRNs.
method Information geometric approach using natural gradient.
result Validated through numerical simulations, demonstrating faster convergence in specific CRNs.
STNN-DDI predicts drug interactions using substructure-aware neural networks.
problem Predicting drug-drug interactions (DDIs) to avoid side effects in poly-drug treatments.
method Designing a novel Substructure-ware Tensor Neural Network (STNN-DDI) that learns a 3-D tensor of substructure-substructure interactions.
result Significant improvement in AUC, AUPR, Accuracy, and Precision compared to state-of-the-art models.