Chemistry in Times of Artificial Intelligence.

Chemists have to a large extent gained their knowledge by doing experiments and thus gather data. By putting various data together and then analyzing them, chemists have fostered their understanding of chemistry. Since the 1960s, computer methods have been developed to perform this process from data to information to knowledge. Simultaneously, methods were developed for assisting chemists in solving their fundamental questions such as the prediction of chemical, physical, or biological properties, the design of organic syntheses, and the elucidation of the structure of molecules. This eventually led to a discipline of its own: chemoinformatics. Chemoinformatics has found important applications in the fields of drug discovery, analytical chemistry, organic chemistry, agrichemical research, food science, regulatory science, material science, and process control. From its inception, chemoinformatics has utilized methods from artificial intelligence, an approach that has recently gained more momentum.

Leaving us with fond memories, smiles, SMILES and, alas, tears: a tribute to David Weininger, 1952-2016.

David Weininger's career, accomplishments, genius, and friendship are warmly remembered by several of his colleagues, friends, and admirers.

Chemoinformatics: Achievements and Challenges, a Personal View.

Chemoinformatics provides computer methods for learning from chemical data and for modeling tasks a chemist is facing. The field has evolved in the past 50 years and has substantially shaped how chemical research is performed by providing access to chemical information on a scale unattainable by traditional methods. Many physical, chemical and biological data have been predicted from structural data. For the early phases of drug design, methods have been developed that are used in all major pharmaceutical companies. However, all domains of chemistry can benefit from chemoinformatics methods; many areas that are not yet well developed, but could substantially gain from the use of chemoinformatics methods. The quality of data is of crucial importance for successful results. Computer-assisted structure elucidation and computer-assisted synthesis design have been attempted in the early years of chemoinformatics. Because of the importance of these fields to the chemist, new approaches should be made with better hardware and software techniques. Society's concern about the impact of chemicals on human health and the environment could be met by the development of methods for toxicity prediction and risk assessment. In conjunction with bioinformatics, our understanding of the events in living organisms could be deepened and, thus, novel strategies for curing diseases developed. With so many challenging tasks awaiting solutions, the future is bright for chemoinformatics.

New publicly available chemical query language, CSRML, to support chemotype representations for application to data mining and modeling.

Chemotypes are a new approach for representing molecules, chemical substructures and patterns, reaction rules, and reactions. Chemotypes are capable of integrating types of information beyond what is possible using current representation methods (e.g., SMARTS patterns) or reaction transformations (e.g., SMIRKS, reaction SMILES). Chemotypes are expressed in the XML-based Chemical Subgraphs and Reactions Markup Language (CSRML), and can be encoded not only with connectivity and topology but also with properties of atoms, bonds, electronic systems, or molecules. CSRML has been developed in parallel with a public set of chemotypes, i.e., the ToxPrint chemotypes, which are designed to provide excellent coverage of environmental, regulatory, and commercial-use chemical space, as well as to represent chemical patterns and properties especially relevant to various toxicity concerns. A software application, ChemoTyper has also been developed and made publicly available in order to enable chemotype searching and fingerprinting against a target structure set. The public ChemoTyper houses the ToxPrint chemotype CSRML dictionary, as well as reference implementation so that the query specifications may be adopted by other chemical structure knowledge systems. The full specifications of the XML-based CSRML standard used to express chemotypes are publicly available to facilitate and encourage the exchange of structural knowledge.

ChemStable: a web server for rule-embedded naïve Bayesian learning approach to predict compound stability.

Predicting compound chemical stability is important because unstable compounds can lead to either false positive or to false negative conclusions in bioassays. Experimental data (COMDECOM) measured from DMSO/H2O solutions stored at 50 °C for 105 days were used to predicted stability by applying rule-embedded naïve Bayesian learning, based upon atom center fragment (ACF) features. To build the naïve Bayesian classifier, we derived ACF features from 9,746 compounds in the COMDECOM dataset. By recursively applying naïve Bayesian learning from the data set, each ACF is assigned with an expected stable probability (p(s)) and an unstable probability (p(uns)). 13,340 ACFs, together with their p(s) and p(uns) data, were stored in a knowledge base for use by the Bayesian classifier. For a given compound, its ACFs were derived from its structure connection table with the same protocol used to drive ACFs from the training data. Then, the Bayesian classifier assigned p(s) and p(uns) values to the compound ACFs by a structural pattern recognition algorithm, which was implemented in-house. Compound instability is calculated, with Bayes' theorem, based upon the p(s) and p(uns) values of the compound ACFs. We were able to achieve performance with an AUC value of 84% and a tenfold cross validation accuracy of 76.5%. To reduce false negatives, a rule-based approach has been embedded in the classifier. The rule-based module allows the program to improve its predictivity by expanding its compound instability knowledge base, thus further reducing the possibility of false negatives. To our knowledge, this is the first in silico prediction service for the prediction of the stabilities of organic compounds.

Chemoinformatics and artificial intelligence colloquium: progress and challenges in developing bioactive compounds.

We report the main conclusions of the first Chemoinformatics and Artificial Intelligence Colloquium, Mexico City, June 15-17, 2022. Fifteen lectures were presented during a virtual public event with speakers from industry, academia, and non-for-profit organizations. Twelve hundred and ninety students and academics from more than 60 countries. During the meeting, applications, challenges, and opportunities in drug discovery, de novo drug design, ADME-Tox (absorption, distribution, metabolism, excretion and toxicity) property predictions, organic chemistry, peptides, and antibiotic resistance were discussed. The program along with the recordings of all sessions are freely available at https://www.difacquim.com/english/events/2022-colloquium/ .

Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information.

The Online Chemical Modeling Environment is a web-based platform that aims to automate and simplify the typical steps required for QSAR modeling. The platform consists of two major subsystems: the database of experimental measurements and the modeling framework. A user-contributed database contains a set of tools for easy input, search and modification of thousands of records. The OCHEM database is based on the wiki principle and focuses primarily on the quality and verifiability of the data. The database is tightly integrated with the modeling framework, which supports all the steps required to create a predictive model: data search, calculation and selection of a vast variety of molecular descriptors, application of machine learning methods, validation, analysis of the model and assessment of the applicability domain. As compared to other similar systems, OCHEM is not intended to re-implement the existing tools or models but rather to invite the original authors to contribute their results, make them publicly available, share them with other users and to become members of the growing research community. Our intention is to make OCHEM a widely used platform to perform the QSPR/QSAR studies online and share it with other users on the Web. The ultimate goal of OCHEM is collecting all possible chemoinformatics tools within one simple, reliable and user-friendly resource. The OCHEM is free for web users and it is available online at http://www.ochem.eu.

IGERS: inferring Gibbs energy changes of biochemical reactions from reaction similarities.

Mathematical analysis and modeling of biochemical reaction networks requires knowledge of the permitted directionality of reactions and membrane transport processes. This information can be gathered from the standard Gibbs energy changes (DeltaG(0)) of reactions and the concentration ranges of their reactants. Currently, experimental DeltaG(0) values are not available for the vast majority of cellular biochemical processes. We propose what we believe to be a novel computational method to infer the unknown DeltaG(0) value of a reaction from the known DeltaG(0) value of the chemically most similar reaction. The chemical similarity of two arbitrary reactions is measured by the relative number (T) of co-occurring changes in the chemical attributes of their reactants. Testing our method across a validated reference set of 173 biochemical reactions with experimentally determined DeltaG(0) values, we found that a minimum reaction similarity of T = 0.6 is required to infer DeltaG(0) values with an error of <10 kJ/mol. Applying this criterion, our method allows us to assign DeltaG(0) values to 458 additional reactions of the BioPath database. We believe our approach permits us to minimize the number of DeltaG(0) measurements required for a full coverage of a given reaction network with reliable DeltaG(0) values.

Similarity perception of reactions catalyzed by oxidoreductases and hydrolases using different classification methods.

In this work, the perception of similarity of reactions catalyzed by hydrolases and oxidoreductases on the basis of the overall breaking and making of bonds of reactions is investigated. Six physicochemical properties for the reacting bond in the substrate of each enzymatic reaction were calculated to describe the characteristics of each reaction. The 311 reactions catalyzed by hydrolases (EC 3.b.c.d) and the 651 reactions catalyzed by oxidoreductases (EC 1.b.c.d) were classified by Kohonen's self-organizing neural network (KohNN), by a support vector machine (SVM), and by hierarchical clustering analysis (HCA). For the 311 reactions catalyzed by hydrolases, the classification accuracy of 95.8% by a KohNN and 97.7% by an SVM was achieved. For the 651 reactions catalyzed by oxidoreductases, the classification accuracy was 93.4% and 96.3% by a KohNN and a SVM, respectively. The similarities of reactions reflected by the physicochemical effects of reacting bonds were compared with the traditional Enzyme Commission (EC) classification system. The results of a KohNN and a SVM are similar to those of the EC classification system method. However, the perception of similarity of reactions by a KohNN and a SVM shows finer details of the enzymatic reactions and thus could provide a good basis for the comparison of enzymes.

Performance of radial distribution function-based descriptors in the chemoinformatic studies of HIV-1 protease.

Aim: This letter investigates the role of radial distribution function-based descriptors for in silico design of new drugs. Methodology: The multiple linear regression models for HIV-1 protease and its complexes with a series of inhibitors were constructed. A detailed analysis of major atomic contributions to the radial distribution function descriptor weighted by the number of valence shell electrons identified residues Arg8, Asp29 and residues of the catalytic triad as crucial for the correlation with the inhibition constant, together with residues Asp30 and Ile50, whose mutations are known to cause an emergence of drug resistant variants. Conclusion: This study demonstrates an easy and fast assessment of the activity of potential drugs and the derivation of structural information of their complexes with the receptor or enzyme.

An environmental perspective on large-scale genome clustering based on metabolic capabilities.

MOTIVATION: In principle, an organism's ability to survive in a speci.c environment, is an observable result of the organism's regulatory and metabolic capabilities. Nonetheless, current knowledge about the global relation of the metabolisms and the niches of organisms is still limited. RESULTS: In order to further investigate this relation, we grouped species showing similar metabolic capabilities and systematically mapped their habitats onto these groups. For this purpose, we predicted the metabolic capabilities for 214 sequenced genomes. Based on these predictions, we grouped the genomes by hierarchical clustering. Finally, we mapped different environmental conditions and diseases related to the genomes onto the resulting clusters. This mapping uncovered several conditions and diseases that were unexpectedly enriched in clusters of metabolically similar species. As an example, Encephalitozoon cuniculi--a microsporidian causing a multisystemic disease accompanied by CNS problems in rabbits--occurred in the same metabolism-based cluster as bacteria causing similar symptoms in humans. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

COMDECOM: predicting the lifetime of screening compounds in DMSO solution.

The technological evolution of the 1990s in both combinatorial chemistry and high-throughput screening created the demand for rapid access to the compound deck to support the screening process. The common strategy within the pharmaceutical industry is to store the screening library in DMSO solution. Several studies have shown that a percentage of these compounds decompose in solution, varying from a few percent of the total to a substantial part of the library. In the COMDECOM (COMpound DECOMposition) project, the compound stability of screening compounds in DMSO solution is monitored in an accelerated thermal, hydrolytic, and oxidative decomposition program. A large database with stability data is collected, and from this database, a predictive model is being developed. The aim of this program is to build an algorithm that can flag compounds that are likely to decompose-information that is considered to be of utmost importance (e.g., in the compound acquisition process and when evaluation screening results of library compounds, as well as in the determination of optimal storage conditions).

Comparison of multilabel and single-label classification applied to the prediction of the isoform specificity of cytochrome p450 substrates.

Each drug can potentially be metabolized by different CYP450 isoforms. In the development of new drugs, the prediction of the metabolic fate is important to prevent drug-drug interactions. In the present study, a collection of 580 CYP450 substrates is deeply analyzed by applying multi- and single-label classification strategies, after the computation and selection of suitable molecular descriptors. Cross-training with support vector machine, multilabel k-nearest-neighbor and counterpropagation neural network modeling methods were used in the multilabel approach, which allows one to classify the compounds simultaneously in multiple classes. In the single-label models, automatic variable selection was combined with various cross-validation experiments and modeling techniques. Moreover, the reliability of both multi- and single-label models was assessed by the prediction of an external test set. Finally, the predicted results of the best models were compared to show that, even if the models present similar performances, the multilabel approach more coherently reflects the real metabolism information.

Exploring potency and selectivity receptor antagonist profiles using a multilabel classification approach: the human adenosine receptors as a key study.

Nowadays, in medicinal chemistry adenosine receptors represent some of the most studied targets, and there is growing interest on the different adenosine receptor (AR) subtypes. The AR subtypes selectivity is highly desired in the development of potent ligands to achieve the therapeutic success. So far, very few ligand-based strategies have been investigated to predict the receptor subtypes selectivity. In the present study, we have carried out a novel application of the multilabel classification approach by combining our recently reported autocorrelated molecular descriptors encoding for the molecular electrostatic potential (autoMEP) with support vector machines (SVMs). Three valuable models, based on decreasing thresholds of potency, have been generated as in series quantitative sieves for the simultaneous prediction of the hA(1)R, hA(2A)R, hA(2B)R, and hA(3)R subtypes potency profile and selectivity of a large collection, more than 500, of known inverse agonists such as xanthine, pyrazolo-triazolo-pyrimidine, and triazolo-pyrimidine analogues. The robustness and reliability of our multilabel classification models were assessed by predicting an internal test set. Finally, we have applied our strategy to 13 newly synthesized pyrazolo-triazolo-pyrimidine derivatives inferring their full adenosine receptor potency spectrum and hAR subtypes selectivity profile.

Second-generation de novo design: a view from a medicinal chemist perspective.

For computational de novo design, a general retrospective validation work is a very challenging task. Here we propose a comprehensive workflow to de novo design driven by the needs of computational and medicinal chemists and, at the same time, we propose a general validation scheme for this technique. The study was conducted combining a suite of already published programs developed within the framework of the NovoBench project, which involved three different pharmaceutical companies and four groups of developers. Based on 188 PDB protein-ligand complexes with diverse functions, the study involved the ligand reconstruction by means of a fragment-based de-novo design approach. The structure-based de novo search engine FlexNovo showed in five out of eight total cases the ability to reconstruct native ligands and to rank them in four cases out of five within the first five candidates. The generated structures were ranked according to their synthetic accessibilities evaluated by the program SYLVIA. This investigation showed that the final candidate molecules have about the same synthetic complexity as the respective reference ligands. Furthermore, the plausibility of being true actives was assessed through literature searches.

Neural networks as valuable tools to differentiate between sesquiterpene lactones' inhibitory activity on serotonin release and on NF-kappaB.

Sesquiterpene lactones are the active components of a variety of medicinal plants from the Asteraceae family. They possess biological activities such as the inhibition of NF-kappaB and the release inhibition of the vasoactive serotonin. On the basis of a data set of 54 SLs, we report the development of a quantitative model for the prediction of serotonin release inhibition. Comparing this model with a previous investigation of the target NF-kappaB, structural features necessary for specific compounds could be acquired. Atomic properties encoded by radial distribution function and molecular surface potentials encoded by autocorrelation were used as descriptors. Whereas some descriptors describe the structural requirements for both activities, other descriptors can be used to decide whether an SL is more active to NF-kappaB or to serotonin release. Again, counter propagation neural networks proved to be a valuable tool to establish structure-activity relationships that are necessary for the search for and optimization of lead structures.

Simple method for the prediction of the separation of racemates with high-performance liquid chromatography on Whelk-O1 chiral stationary phase.

A simple method for the prediction of whether or not a racemate can be separated on a Whelk-O1 chiral stationary phase has been developed. In this approach, molecules are represented by counting the number of atom types of the neighbors spheres of the chiral center. A decision tree is then used to decide based on a few of these atom count descriptors whether a given racemate can be separated. High values of correct prediction were obtained, namely with more than 94% for training sets and of about 90% for cross-validation results. The same rate of correct prediction was also obtained on an external data set. The descriptors can be rapidly and easily retrieved by just counting the atom types around the chiral center by inspecting the chemical diagram of the molecule. Furthermore, the decision tree model can be applied through the use of a small set of rules that eventually predicts whether or not a racemate is separated. Due to its computational simplicity, the procedure is of interest for experimentalists that need to make rapid assessment of the separation without having to program or input complex formulas.

Explorations into modeling human oral bioavailability.

Explorations into modeling human oral bioavailability started with a whole dataset of 772 drug compounds. First, training set and test set were chosen based on Kohonen's self-organizing Neural Network (KohNN). Then, a quantitative model of the whole dataset was built using multiple linear regression (MLR) analysis. This model had limited predictability emphasizing that a variety of pharmacokinetic factors influence human oral bioavailability. In order to explore whether better models can be built when the compounds share some ADME properties, four subsets were chosen from the whole dataset to build quantitative models and better models were obtained by MLR analysis. These studies show that, indeed, good models for predicting human oral bioavailability can be obtained from datasets sharing certain pharmacokinetic properties.

Self-organizing maps for identification of new inhibitors of P-glycoprotein.

Self-organizing maps were trained to separate high- and low-active propafenone-type inhibitors of P-glycoprotein. The trained maps were subsequently used to identify highly active compounds in a virtual screen of the SPECS compound library.

Development of a structural model for NF-kappaB inhibition of sesquiterpene lactones using self-organizing neural networks.

A variety of sesquiterpene lactones (SLs) possess considerable anti-inflammatory activity. Several studies have shown that they exert this effect in part by inhibiting the activation of the transcription factor NF-kappaB. In the present study we elaborated on the investigation of a data set of 103 structurally diverse SLs for which we had previously developed several different QSAR equations dependent on the skeletal type. Use of 3D structure descriptors resulted in a single model for the entire data set. In particular, local radial distribution functions (L-RDF) were used that centered on the methylene-carbonyl substructure believed to be the site of attack of cysteine-38 of the p65/NF-kappaB subunit. The model was developed by using a counterpropagation neural network (CPGNN), attesting to the power of this method for establishing structure-activity-relationships. The investigations shed more light onto the influence of the chemical structure on NF-kappaB inhibitory activity.