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Specific lipid and protein changes characterized term preeclampsia (PRE) and gestational diabetes mellitus (GDM) and novel visualization tools were created to aid in the process.
The recent advances in deep learning, generative modeling, and statistical learning have ushered in a renewed interest in traditional cheminformatics tools and methods. Quantifying molecular similarity is essential in molecular generative modeling, exploratory molecular synthesis campaigns, and drug-discovery applications to assess how new molecules differ from existing ones. Further, most tools target advanced users and lack general implementations accessible to the larger community. In this work, we introduce Artificial Intelligence Molecular Similarity (AIMSim), an accessible cheminformatics platform for performing similarity operations on collections of molecules called molecular datasets. AIMSim provides a unified platform to perform similarity-based tasks on molecular datasets, such as diversity quantification, outlier and novelty analysis, clustering, dimensionality reduction, and inter-molecular comparisons. AIMSim implements all major binary similarity metrics and molecular fingerprints and is provided as a Python package that includes support for command-line use as well as a Graphical User Interface for code-free utilization with fully interactive plots.
We present a comprehensive, in silico-based discovery approach to identifying novel liquid organic hydrogen carrier (LOHC) candidates using cheminformatics methods and quantum chemical calculations. We screened over 160 billion molecules from ZINC15 and GDB-17 chemical databases for structural similarity to known LOHCs and employed a data-driven selection criterion connecting molecular features with dehydrogenation enthalpy. This scoring criterion effectively predicts dehydrogenation enthalpies from SMILES strings, streamlining the LOHC screening process. After rigorous screening and down-selection, we compiled a database of 3000 dehydrogenation reactions for the most promising LOHC candidates, setting the stage for future selection based on kinetics and catalysis. This work demonstrates the significant impact of integrating quantum chemistry and cheminformatics in materials discovery, accelerating the selection process while reducing experimental efforts and time. By proposing new molecules as prospective LOHC candidates, our study provides a valuable resource for researchers and engineers in the development of advanced LOHC systems and showcases a successful approach for high-throughput discovery, contributing to more efficient and sustainable energy storage solutions.
The surprising effectiveness of topology in the chemical sciences: graphlets in our open-source library, , provide accurate white-box 2D chemical property prediction.
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Accurately predicting small molecule partitioning and hydrophobicity is critical in the drug discovery process. There are many heterogeneous chemical environments within a cell and entire human body. For example, drugs must be able to cross the hydrophobic cellular membrane to reach their intracellular targets, and hydrophobicity is an important driving force for drug–protein binding. Atomistic molecular dynamics (MD) simulations are routinely used to calculate free energies of small molecules binding to proteins, crossing lipid membranes, and solvation but are computationally expensive. Machine learning (ML) and empirical methods are also used throughout drug discovery but rely on experimental data, limiting the domain of applicability. We present atomistic MD simulations calculating 15,000 small molecule free energies of transfer from water to cyclohexane. This large data set is used to train ML models that predict the free energies of transfer. We show that a spatial graph neural network model achieves the highest accuracy, followed closely by a 3D-convolutional neural network, and shallow learning based on the chemical fingerprint is significantly less accurate. A mean absolute error of ~4 kJ/mol compared to the MD calculations was achieved for our best ML model. We also show that including data from the MD simulation improves the predictions, tests the transferability of each model to a diverse set of molecules, and show multitask learning improves the predictions. This work provides insight into the hydrophobicity of small molecules and ML cheminformatics modeling, and our data set will be useful for designing and testing future ML cheminformatics methods.
Minervachem is a tool for cheminformatics and machine learning in chemistry. It includes both algorithms from existing literature and algorithms which we have developed. Its features center around two main themes: molecular representations and algorithms, both for molecular machine learning. It provides a scikit-learn transformer interface for molecular featurization and an estimator interface for machine learning algorithms. It also provides visualization tools for explaining machine learning predictions. We aim to continue developing this software to improve its performance and usability and expand its capabilities within the realm of molecular machine learning, cheminformatics, and visualization.
Enzyme substrate promiscuity has significant implications for metabolic engineering. The ability to predict the space of possible enzymatic side reactions is crucial for elucidating underground metabolic networks in microorganisms, as well as harnessing novel biosynthetic capabilities of enzymes to produce desired chemicals. Reaction rule-based cheminformatics platforms have been implemented to computationally enumerate possible promiscuous reactions, relying on existing knowledge of enzymatic transformations to inform novel reactions. However, past versions of curated reaction rules have been limited by a lack of comprehensiveness in representing all possible transformations, as well as the need to prune rules to enhance computational efficiency in pathway expansion. To this end, we curated a set of 1224 most generalized reaction rules, automatically abstracted from atom-mapped MetaCyc reactions and verified to uniquely cover all common enzymatic transformations. Additionally, we developed a framework to systematically identify and correct redundancies and errors in the curation process, resulting in a minimal, yet comprehensive, rule set. These reaction rules were capable of reproducing more than 85% of all reactions in the KEGG and BRENDA databases, for which a large fraction of reactions is not present in MetaCyc. Our rules exceed all previously published rule sets for which reproduction was possible in this coverage analysis, which allows for the exploration of a larger space of known enzymatic transformations. By leveraging the entire knowledge of possible metabolic reactions through generalized enzymatic reaction rules, we are able to better utilize underground metabolic pathways and accelerate novel biosynthetic pathway design to enable bioproduction towards a wider range of new molecules.
In silico metabolism prediction is a cheminformatic task of autonomously predicting the set of metabolic byproducts produced from a specified molecule and a set of enzymes or reactions. Here we describe a novel machine-learned in silico cytochrome P450 (CYP450) metabolism prediction suite, called CyProduct, that accurately predicts metabolic byproducts for a specified molecule and a human CYP450 isoform. It includes three modules: (1) CypReact, a tool that predicts if the query compound reacts with a given CYP450 enzyme; (2) CypBoM, a tool that accurately predicts the “bond site” of the reaction (i.e., which specific bonds within the query molecule react with the CYP isoform); and (3) MetaboGen, a tool that generates the metabolic byproducts based on CypBoM’s bond-site prediction. CyProduct predicts metabolic biotransformation products for each of the nine most important human CYP450 enzymes. CypBoM uses an important new concept called “Bond of Metabolism” (BoM), which extends the traditional “Site of Metabolism" (SoM) by specifying the information about the set of chemical bonds that is modified or formed in a metabolic reaction (rather than the specific atom). We created a BoM database for 3487 CYP450-mediated Phase I reactions, then used this to train the CypBoM Predictor to predict the reactive bond locations on substrate molecules. CypBoM Predictor’s cross-validated Jaccard score for reactive bond prediction ranged from 0.380 to 0.452 over the nine CYP450 enzymes. Over variants of a test set of 72 known CYP450 substrates and 30 non-reactants, CyProduct outperformed the other packages -- including ADMET Predictor, BioTransformer and GLORY -- by an average of 200% (wrt Jaccard score) in terms of predicting metabolites. The CyProduct suite and the datasets are freely available at https://bitbucket.org/wishartlab/cyproduct/src/master/.
Catalysis informatics is a distinct sub-field that lies at the intersection of cheminformatics and materials informatics, but with unique challenges arising from the dynamic, surface-sensitive, multi-scale nature of heterogeneous catalysis. The ideas behind catalysis informatics can be traced back decades, but the field is only recently emerging due to advances in data infrastructure, statistics, machine learning, and computational methods. In this work we review the field from early works on expert systems and knowledge engines to more recent approaches utilizing machine-learning and uncertainty quantification. The data-information-knowledge hierarchy is introduced and used to classify various developments. The chemical master equation and micro-kinetic models are proposed as a quantitative representation of catalysis knowledge, which can be used to generate explanative and predictive hypotheses for the understanding and discovery of catalytic materials. We discuss future prospects for the field, including improved quantitative coupling of experiment/theory, advanced micro-kinetic models, and the development of open-source software tools. As a result, integration of existing chemical and physical models with emerging statistical and computational tools presents a promising route toward the automated design, discovery, and optimization of heterogeneous catalytic processes.
Exploratory analysis of the chemical space is an important task in the field of cheminformatics. For example, in drug discovery research, chemists investigate sets of thousands of chemical compounds in order to identify novel yet structurally similar synthetic compounds to replace natural products. Manually exploring the chemical space inhabited by all possible molecules and chemical compounds is impractical, and therefore presents a challenge. To fill this gap, we present ChemoGraph, a novel visual analytics technique for interactively exploring related chemicals. In ChemoGraph, we formalize a chemical space as a hypergraph and apply novel machine learning models to compute related chemical compounds. It uses a database to find related compounds from a known space and a machine learning model to generate new ones, which helps enlarge the known space. Moreover, ChemoGraph highlights interactive features that support users in viewing, comparing, and organizing computationally identified related chemicals. With a drug discovery usage scenario and initial expert feedback from a case study, we demonstrate the usefulness of ChemoGraph.
It is increasingly clear that (a) many metabolites undergo spontaneous or enzyme-catalyzed side reactions in vivo, (b) the damaged metabolites formed by these reactions can be harmful, and (c) organisms have biochemical systems that limit the buildup of damaged metabolites. These damage-control systems either return a damaged molecule to its pristine state (metabolite repair) or convert harmful molecules to harmless ones (damage preemption). Because all organisms share a core set of metabolites that suffer the same chemical and enzymatic damage reactions, certain damage-control systems are widely conserved across the kingdoms of life. Relatively few damage reactions and damage-control systems are well known. Uncovering new damage reactions and identifying the corresponding damaged metabolites, damage-control genes, and enzymes demands a coordinated mix of chemistry, metabolomics, cheminformatics, biochemistry, and comparative genomics. This review illustrates the above points using examples from plants, which are at least as prone to metabolite damage as other organisms.
Accurately classifying chemical structures is essential for cheminformatics and bioinformatics, including tasks such as identifying bioactive compounds of interest, screening molecules for toxicity to humans, finding non-organic compounds with desirable material properties, or organizing large chemical libraries for drug discovery or environmental monitoring. However, manual classification is labor-intensive and difficult to scale to large chemical databases. Existing automated approaches either rely on manually constructed classification rules, or are deep learning methods that lack explainability. This work presents an approach that uses generative artificial intelligence to automatically write chemical classifier programs for classes in the Chemical Entities of Biological Interest (ChEBI) database. These programs can be used for efficient deterministic run-time classification of SMILES structures, with natural language explanations. The programs themselves constitute an explainable computable ontological model of chemical class nomenclature, which we call the ChEBI Chemical Class Program Ontology (C3PO). We validated our approach against the ChEBI database, and compared our results against deep learning models and a naive SMARTS pattern based classifier. C3PO outperforms the naive classifier, but does not reach the performance of state of the art deep learning methods. However, C3PO has a number of strengths that complement deep learning methods, including explainability and reduced data dependence. C3PO can be used alongside deep learning classifiers to provide an explanation of the classification, where both methods agree. The programs can be used as part of the ontology development process, and iteratively refined by expert human curators.
The rise and fall of artificial neural networks is well documented in the scientific literature of both the fields of computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning, a machine learning algorithm based on “deep” neural networks. Within the last few years, we have seen the transformative impact of deep learning the computer science domain, notably in speech recognition and computer vision, to the extent that the majority of practitioners in those field are now regularly eschewing prior established models in favor of deep learning models. In this review, we provide an introductory overview into the theory of deep neural networks and their unique properties as compared to traditional machine learning algorithms used in cheminformatics. By providing an overview of the variety of emerging applications of deep neural networks, we highlight its ubiquity and broad applicability to a wide range of challenges in the field, including QSAR, virtual screening, protein structure modeling, QM calculations, materials synthesis and property prediction. In reviewing the performance of deep neural networks, we observed a consistent outperformance against non neural networks state-of-the-art models across disparate research topics, and deep neural network based models often exceeded the “glass ceiling” expectations of their respective tasks. Coupled with the maturity of GPU-accelerated computing for training deep neural networks and the exponential growth of chemical data on which to train these networks on, we anticipate that deep learning algorithms will be a useful tool and may grow into a pivotal role for various challenges in the computational chemistry field.
Abstract The Protein Data Bank (PDB), established in 1971, is the primary global, open‐access archive for experimentally determined 3D macromolecular structures (proteins, RNA, DNA). The research‐focused RCSB.org web‐portal provides access to these data alongside more than one million machine‐learning‐predicted structure models, greatly expanding the available structural landscape. Rapid growth of both experimental and computational structures has increased the need for powerful yet accessible search tools that serve a broad and diverse scientific community. Herein, we describe a redesigned RCSB Protein Data Bank RCSB.org Advanced Search capability that supports intuitive discovery of 3D structures through a unified interface. This interface integrates annotation‐, sequence‐, and 3D structure‐based searches, embeds an interactive 3D viewer, and incorporates curated biological knowledge, such as catalytic site definitions from Mechanism and Catalytic Site Atlas and ligand‐guided structural motifs, for constructing geometry‐driven queries. A new Chemical Search tool allows definition of chemical queries via an integrated drawing tool or standard identifiers, seamlessly combining them with annotation filters. By allowing query definition directly within spatial and chemical contexts, these search interfaces reduce the need for detailed knowledge of residue numbering, chain identifiers, or external cheminformatics software. This capability enables efficient exploration of structures, chemical diversity, and structure–function relationships across all life domains. The redesigned interfaces can be accessed directly at rcsb.org/search/advanced for Advanced Search and rcsb.org/search/chemical for Chemical Search.