Schon in den 1990er Jahren sparte künstliche Intelligenz der Industrie Geld und der Forschung Nerven. Der Autor des ersten Lehrbuchs zu neuronalen Netzen in der Chemie zeigt: Mit acht Inhaltsstoffen von Olivenöl lässt sich eine Karte Italiens zeichnen.

This paper gives an overview of the lectures and posters presented at the 8th Autumn School in Chemoinformatics held in Nara, Japan on 28th - 30th November 2023. The topics ranged from the study of chemical reactions through drug design and the use of Chemical Language Models and electronic structure informatics to the modeling of materials. In addition, a brief overview of the 50 years of work in chemoinformatics by Johann Gasteiger is given with an emphasis on the essential decisions during his scientific career.

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/

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.

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.

The achievements of Professor Kimito Funatsu for the development of chemoinformatics in Japan are briefly summarized. Furthermore, some aspects of the collaboration of this author with Kimito Funatsu are discussed.

The achievements of Professor Kimito Funatsu for the development of chemoinformatics in Japan are briefly summarized Furthermore, some aspects of the collaboration of this author with Kimito Funatsu are discussed.

Background: Potency is the broadest available biological activity data type. In turn, Ligand Efficiency (LE) is a molecular descriptor that probes the ratio of potency vs Heavy Atom Count (HAC), which emphasizes low HAC more than potency and thus has drawbacks as an estimator of drug candidates. The objective was to design a novel transform to probe potency and HAC interaction in which potency and HAC would be balanced more evenly. Methods: In this study, potency data of ChEMBL, PubChem, FDA approvals and drug (fragments) were analysed. A novel descriptor, a product of the pAC(50) value with HAC, multiplicative or Product Ligand Efficiency (PLE) was designed and tested. Results: In particular PLE was compared with pAC(50) and LE vs the HAC statistics for different series of ligands. This indicated that PLE is an informative estimator that can be used to recognize the potential of drugs. PLE has a maximum value in the range around 30-50 HAC. Conclusion: Drug design is a complex problem. Similarly, to drug-likeness, LE prefers small molecules. This makes LE a tool serendipitously improving drug likeness. In this context, LE performs unexpectedly well even despite the uncertainty of its physical meaning. PLE is a more evenly balanced estimator whose physical meaning is the Minimum Inhibitory Concentration (MIC).

Ligand efficiency (LE) is a molecular descriptor that probes the ratio of potency vs. heavy atom count (HAC). As an estimator of drug candidates, LE emphasizes a low heavy atom count more than potency. The objective was to design a novel transform where potency and the HAC would be balanced more evenly. A series of novel descriptors SCORE were defined to evaluate the co-influence of potency and the HAC. In particular, the product ligand efficiency (PLE) was designed and tested using the data of the ChEMBL, PubChem as well as the selected series of drugs and drug-fragments.

Ligand efficiency (LE) is a molecular descriptor that probes the ratio of potency vs. heavy atom count (HAC). As an estimator of drug candidates, LE emphasizes a low heavy atom count more than potency. The objective was to design a novel transform where potency and the HAC would be balanced more evenly. A series of novel descriptors SCORE were defined to evaluate the co-influence of potency and the HAC. In particular, the product ligand efficiency (PLE) was designed and tested using the data of the ChEMBL, PubChem as well as the selected series of drugs and drug-fragments.

Chapter 3 Representation of Chemical Reactions Prof. Dr. Johann Gasteiger, Prof. Dr. Johann Gasteiger Computer-Chemie-Centrum and Institute of Organic Chemistry, Friedrich-Alexander-University of Erlangen-Nuernberg, Naegelsbachstrasse 25, 91052 Erlangen, GermanySearch for more papers by this author Prof. Dr. Johann Gasteiger, Prof. Dr. Johann Gasteiger Computer-Chemie-Centrum and Institute of Organic Chemistry, Friedrich-Alexander-University of Erlangen-Nuernberg, Naegelsbachstrasse 25, 91052 Erlangen, GermanySearch for more papers by this author Book Editor(s):Prof. Dr. Johann Gasteiger, Prof. Dr. Johann Gasteiger Computer-Chemie-Centrum and Institute of Organic Chemistry, Friedrich-Alexander-University of Erlangen-Nuernberg, Naegelsbachstrasse 25, 91052 Erlangen, GermanySearch for more papers by this authorDr. Thomas Engel, Dr. Thomas Engel Computer-Chemie-Centrum and Institute of Organic Chemistry, Friedrich-Alexander-University of Erlangen-Nuernberg, Naegelsbachstrasse 25, 91052 Erlangen, GermanySearch for more papers by this author First published: 25 September 2003 https://doi.org/10.1002/3527601643.ch3Citations: 2 AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Summary This chapter contains sections titled: Introduction Reaction Types Reaction Center Chemical Reactivity Physicochemical Effects Simple Approaches to Quantifying Chemical Reactivity Reaction Classification Model-Driven Approaches Data-Driven Approaches Stereochemistry of Reactions Tutorial: Stereochemistry of Reactions Citing Literature Chemoinformatics: A Textbook RelatedInformation

The knowledge of physical and chemical properties of a compound is required for understanding and modeling the action of a compound in drug discovery, environmental chemistry, and other chemical industries. This chapter first provides an overview of typical methods for the prediction of physicochemical properties and gives a more detailed analysis of several physicochemical properties important for drug discovery and chemical industry in general. It then exemplifies the modeling of several properties, that is, octanol/water partition and distribution coefficients, water solubility, melting point (MP), pKa value, and thermochemical properties. Many physicochemical properties of compounds are strongly interconnected. The relationships between physicochemical properties can be derived based on a theoretical analysis or/and found empirically. The chapter helps the readers to derive quantitative relationships between a property and a structure, and also it summarizes the limitations of different modeling techniques.

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

Chapter 4.1 Chemical Reactions – An Introduction Johann Gasteiger, Computer-Chemie-Centrum, Universität Erlangen-Nürnberg, Nägelsbachstr. 25, 91052 Erlangen, GermanySearch for more papers by this author Johann Gasteiger, Computer-Chemie-Centrum, Universität Erlangen-Nürnberg, Nägelsbachstr. 25, 91052 Erlangen, GermanySearch for more papers by this author Book Editor(s):Thomas Engel, LMU Munich, Department of Chemistry, Butenandtstraße 5-13, 81377 München, GermanySearch for more papers by this authorJohann Gasteiger, University of Erlangen-Nürnberg, Computer-Chemie-Centrum, Nägelsbachstr. 25, 91052 Erlangen, GermanySearch for more papers by this author First published: 20 April 2018 https://doi.org/10.1002/9783527806539.ch4a AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat No abstract is available for this article. Applied Chemoinformatics: Achievements and Future Opportunities RelatedInformation

Representation of Chemical Compounds Book Editor(s):Prof. Dr. Johann Gasteiger, Prof. Dr. Johann Gasteiger Computer-Chemie-Centrum and Institute, of Organic Chemistry, University of Erlangen-Nürnberg, Nägelsbachstraße 25, 91052 Erlangen, GermanySearch for more papers by this author First published: 08 August 2003 https://doi.org/10.1002/9783527618279.part2 AboutPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShareShare a linkShare onFacebookTwitterLinked InRedditWechat Handbook of Chemoinformatics: From Data to Knowledge in 4 Volumes RelatedInformation