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.
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.
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.
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.
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.
ChemNet uses rule-based labels for weakly supervised learning to predict chemical properties.
problem Lack of labeled data in chemistry.
method Rule-based knowledge for training ChemNet, a deep neural network, on large unlabeled chemical databases.
result ChemNet outperforms DNN models trained with conventional supervised learning on smaller datasets.
New neural networks explain quantum chemistry predictions atomically.
problem Need for interpretable quantum chemical models.
method Atomistic neural networks and aggregation of atom-wise contributions.
result Atom-wise explanations reveal chemical insights.
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.
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.
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.
CASTER predicts drug interactions using chemical substructures.
problem Identifying potential drug-drug interactions during drug design.
method CASTER uses sequential pattern mining, auto-encoding, and dictionary learning to predict DDIs.
result CASTER outperformed state-of-the-art models and provided interpretable predictions.
We briefly review recent progress in techniques for modeling and analyzing hyperspectral images and movies, in particular for detecting plumes of both known and unknown chemicals. For detecting chemicals of known spectrum, we extend the technique of using a single subspace for modeling the background to a "mixture of s…
Unified CNN identifies chemical species from Raman spectra.
problem Challenges in identifying chemical species from Raman spectra due to preprocessing requirements.
method A deep convolutional neural network trained to automatically identify substances from Raman spectra.
result Superior classification performance compared to other machine learning methods.
Review of automation's role in chemical discovery, emphasizing future challenges.
problem Improving automation's contribution to chemical discovery.
method Analysis of exemplary studies and open research directions.
result Future autonomous systems need improvement in data handling, model building, and experiment automation.
SMILES2Vec learns chemical properties from SMILES strings without feature engineering.
problem Predicting chemical properties from SMILES strings without manual feature engineering.
method Deep RNN (SMILES2Vec) learns features from SMILES strings, optimized using Bayesian optimization.
result Optimized SMILES2Vec outperforms MLP neural networks and achieves 88% accuracy in predicting solubility.
AI models struggle to generate diverse natural chemical structures.
problem Generating diverse chemical structures for drug discovery.
method Quantified internal chemical diversity; challenge with two models.
result AI models fail to reproduce natural chemical diversity.
ChemBoost predicts protein-ligand binding affinity using SMILES syntax.
problem Predicting high affinity drug-target interactions from sequence similarity alone.
method ChemBoost uses SMILES syntax to represent ligands as documents and proteins as sequences or ligand-centric features. It learns chemical word embeddings and predicts affinities using eXtreme Gradient Boosting.
result ChemBoost outperforms state-of-the-art systems in predicting protein-ligand affinities.
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.
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.
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.
MolGAN generates valid small molecular graphs without graph matching.
problem Generating valid small molecular graphs efficiently.
method Adapts GANs to generate graph-structured data with reinforcement learning.
result MolGAN generates close to 100% valid compounds.
Bayesian optimization improves chemical design by avoiding invalid molecules.
problem Bayesian optimization over variational autoencoder latent space produces invalid molecular structures.
method Formulated constrained Bayesian optimization to avoid querying far from training data.
result Marked improvements in validity of generated molecules.
CrystalGAN generates novel stable chemical compounds using GANs.
problem Generating novel multi-element stable chemical compounds efficiently.
method Cross-domain Generative Adversarial Networks (GANs) with novel architecture and loss functions.
result CrystalGAN generates reasonable data with increased complexity.
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…
Neural network speeds up atmospheric chemistry modeling 4250x.
problem Computational expense of simulating atmospheric chemistry.
method Created a neural network to emulate a complex chemical mechanism.
result Achieved a 250x computational speedup.
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.
AI system synthesizes chemical plant operation procedures for efficiency and stability.
problem Developing efficient and stable operation procedures for complex chemical plants.
method Integrates automated reasoning, deep reinforcement learning, and dynamic simulation with external knowledge.
result Synthesized procedure achieves faster recovery from malfunctions compared to standard PID control.
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 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.
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.
Machine learning speeds up chemical equilibrium calculations in reactive transport simulations.
problem High computational cost of chemical equilibrium calculations in reactive transport models.
method Machine learning method to quickly estimate new equilibrium states based on previous calculations.
result Achieved almost two orders of magnitude speedup in equilibrium calculations.
Heteroencoders improve chemical latent space diversity and molecular generation.
problem Improving chemical latent space properties and diversity in autoencoders.
method Employing SMILES enumeration for encoder or decoder, training RNNs with LSTM, and using QSAR models.
result Heteroencoders yield more diverse latent spaces and better molecular generation.
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.
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…
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.
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.
Multimodal deep learning improves toxicity prediction accuracy.
problem Improving prediction accuracy of chemical compound toxicity.
method Combining multiple neural network types and data representations.
result Significantly better accuracy on a toxicity benchmark.
AI model identifies chemical agents in MCI with high accuracy.
problem Identifying chemical agents in mass casualty incidents.
method Reverse engineered signs/symptoms, trained using ANN, BDT, and WISER.
result WISER outperformed ANN and BDT in identifying chemical agents.
Deep learning models can predict chemical properties without needing advanced chemistry knowledge.
problem How much chemistry does a deep neural network need to know to make accurate predictions?
method Systematically removing and adding localized domain-specific information to image channels of training data.
result An augmented Chemception (AugChemception) outperforms the original model in predicting toxicity, activity, and solvation free energy.
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.
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.
Transfer learning boosts chemically accurate neural network potentials for organic molecules.
problem Developing accurate interatomic potentials from ab-initio data.
method Discriminative fine-tuning of pre-trained neural networks.
result Fine-tuning with energy labels alone can achieve accurate atomic forces.
Deep RL finds efficient pathways for sugar to chemicals.
problem Finding efficient pathways from sugar to value-added chemicals.
method Markov decision process with deep reinforcement learning.
result Promising preliminary results in efficient biomass conversion.
Improved chemical reaction prediction using augmented NLP models.
problem Predicting chemical reactions from text representations.
method Data augmentation and Transformer architecture for SMILES representation.
result Significantly improved accuracy in predicting chemical reactions.
Reactmine infers chemical reactions from time series data, overcoming sparse model limitations.
problem Inferring chemical reaction networks from time series data, especially when initial conditions are not varied.
method Sequential reaction inference in a search tree, ranking and re-optimizing kinetics.
result Reactmine successfully infers preponderant regulations in real datasets, matching model-based analyses.
Model predicts electron paths in chemical reactions.
problem Predicting electron movements in chemical reactions.
method Designing a model to learn electron paths from raw reaction data.
result Model achieves excellent performance on USPTO reaction dataset.