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/ .

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.

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.

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.

Explorations into Chemical Reactions and Biochemical Pathways.

A brief overview of the work in the research group of the present author on extracting knowledge from chemical reaction data is presented. Methods have been developed to calculate physicochemical effects at the reaction site. It is shown that these physicochemical effects can quite favourably be used to derive equations for the calculation of data on gas phase reactions and on reactions in solution such as aqueous acidity of alcohols or carboxylic acids or the hydrolysis of amides. Furthermore, it is shown that these physicochemical effects are quite effective for assigning reactions into reaction classes that correspond to chemical knowledge. Biochemical reactions constitute a particularly interesting and challenging task for increasing our understanding of living species. The BioPath.Database is a rich source of information on biochemical reactions and has been used for a variety of applications of chemical, biological, or medicinal interests. Thus, it was shown that biochemical reactions can be assigned by the physicochemical effects into classes that correspond to the classification of enzymes by the EC numbers. Furthermore, 3D models of reaction intermediates can be used for searching for novel enzyme inhibitors. It was shown in a combined application of chemoinformatics and bioinformatics that essential pathways of diseases can be uncovered. Furthermore, a study showed that bacterial flavor-forming pathways can be discovered.

Classification of Mixtures of Chinese Herbal Medicines Based on a Self-organizing Map (SOM).

Chinese Herbal Medicines (CHMs) are typically mixtures of compounds and are often categorized into cold and hot according to the theory of Chinese Medicine. This classification is essential for guiding the clinical application of CHMs. In this study, three types of molecular descriptors were used to build models for classification of 59 CHMs with typical cold/hot properties in the training set taken from the original records on properties in China Pharmacopeia as reference. The accuracy and the Matthews correlation coefficient of the models were validated by a test set containing other 56 CHMs. The best model produced the accuracies of 94.92 % and 83.93 % on training set and test set, respectively. The MACCS fingerprint model is robust in predicting hot/cold properties of the CHMs from their major constituting compounds. This work shows how a classification model for data consisting of multi-components can be developed. The derived model can be used for the application of Chinese herbal medicines.

Molecular descriptor data explain market prices of a large commercial chemical compound library.

The relationship between the structure and a property of a chemical compound is an essential concept in chemistry guiding, for example, drug design. Actually, however, we need economic considerations to fully understand the fate of drugs on the market. We are performing here for the first time the exploration of quantitative structure-economy relationships (QSER) for a large dataset of a commercial building block library of over 2.2 million chemicals. This investigation provided molecular statistics that shows that on average what we are paying for is the quantity of matter. On the other side, the influence of synthetic availability scores is also revealed. Finally, we are buying substances by looking at the molecular graphs or molecular formulas. Thus, those molecules that have a higher number of atoms look more attractive and are, on average, also more expensive. Our study shows how data binning could be used as an informative method when analyzing big data in chemistry.

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.

Combining chemoinformatics with bioinformatics: in silico prediction of bacterial flavor-forming pathways by a chemical systems biology approach "reverse pathway engineering".

The incompleteness of genome-scale metabolic models is a major bottleneck for systems biology approaches, which are based on large numbers of metabolites as identified and quantified by metabolomics. Many of the revealed secondary metabolites and/or their derivatives, such as flavor compounds, are non-essential in metabolism, and many of their synthesis pathways are unknown. In this study, we describe a novel approach, Reverse Pathway Engineering (RPE), which combines chemoinformatics and bioinformatics analyses, to predict the "missing links" between compounds of interest and their possible metabolic precursors by providing plausible chemical and/or enzymatic reactions. We demonstrate the added-value of the approach by using flavor-forming pathways in lactic acid bacteria (LAB) as an example. Established metabolic routes leading to the formation of flavor compounds from leucine were successfully replicated. Novel reactions involved in flavor formation, i.e. the conversion of alpha-hydroxy-isocaproate to 3-methylbutanoic acid and the synthesis of dimethyl sulfide, as well as the involved enzymes were successfully predicted. These new insights into the flavor-formation mechanisms in LAB can have a significant impact on improving the control of aroma formation in fermented food products. Since the input reaction databases and compounds are highly flexible, the RPE approach can be easily extended to a broad spectrum of applications, amongst others health/disease biomarker discovery as well as synthetic biology.

Self-organizing maps for the classification of gallic acylate polyphenols as HSV-1 inhibitors.

Herpes simplex virus type 1 (HSV-1), a member of the Herpesviridae family, is a ubiquitous, contagious, hostadapted pathogen that causes a wide variety of disease states, such as herpes labialis ("cold sores") and encephalitis. Recently, due to the appearance of acyclovir-resistant HSV-1 mutants, a rapidly growing area of research has been the identification of novel small molecules (whether found in traditional medicine or not) with antiviral activity. One group of these novel pre-drugs is gallic acylate polyphenols. Here, detailed insight into the influence of the chemical structure on anti- HSV-1 activity of gallic acylate polyphenols has been provided based on an exploration of structure-function relationships through self-organizing maps and counterpropagation neural networks. A number of descriptors were investigated to construct optimized models. The resulting model exhibits a correct prediction rate of 90.67%, with active molecule classification accuracy higher than 95.00%, demonstrating that the electrostatic effect and distance between atoms are related to HSV-1 inhibition for these gallic acylate polyphenols. The results provide insights into the influence of the chemical structure on anti-HSV-1 activity of gallic acylate polyphenols.

Solved and Unsolved Problems of Chemoinformatics.

From humble beginnings in the Sixties chemoinformatics evolved into a scientific field of its own. Without the achievements of chemoinformatics the flood of information in chemistry would simply not be manageable and modern research in chemistry and related fields would be inconceivable. However, there are still a host of problems waiting for solutions to be found or to be improved. The impact of chemicals on human health and on the environment presents both challenges and concerns. Research in chemoinformatics could help in better understanding these topics and thus contribute to a better living.

QSAR modeling: where have you been? Where are you going to?

Quantitative structure-activity relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists toward collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making.

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.

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.

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.

Applications of integrated data mining methods to exploring natural product space for acetylcholinesterase inhibitors.

Nature, especially the plant kingdom, is a rich source for novel bioactive compounds that can be used as lead compounds for drug development. In order to exploit this resource, the two neural network-based virtual screening techniques novelty detection with self-organizing maps (SOMs) and counterpropagation neural network were evaluated as tools for efficient lead structure discovery. As application scenario, significant descriptors for acetylcholinesterase (AChE) inhibitors were determined and used for model building, theoretical model validation, and virtual screening. Top-ranked virtual hits from both approaches were docked into the AChE binding site to approve the initial hits. Finally, in vitro testing of selected compounds led to the identification of forsythoside A and (+)-sesamolin as novel AChE inhibitors.