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427 results on '"Johann Gasteiger"'

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

Author

Jürgen Bajorath, Ana L. Chávez-Hernández, Miquel Duran-Frigola, Eli Fernández-de Gortari, Johann Gasteiger, Edgar López-López, Gerald M. Maggiora, José L. Medina-Franco, Oscar Méndez-Lucio, Jordi Mestres, Ramón Alain Miranda-Quintana, Tudor I. Oprea, Fabien Plisson, Fernando D. Prieto-Martínez, Raquel Rodríguez-Pérez, Paola Rondón-Villarreal, Fernanda I. Saldívar-Gonzalez, Norberto Sánchez-Cruz, and Marilia Valli

Subjects
ADME profile, Antibiotic resistance, Artificial intelligence, Career development, Drug discovery, Machine learning, Information technology, T58.5-58.64, Chemistry, QD1-999
Abstract

Abstract 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/ .

Published
2022
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2. Combining chemoinformatics with bioinformatics: in silico prediction of bacterial flavor-forming pathways by a chemical systems biology approach 'reverse pathway engineering'.

Author

Mengjin Liu, Bruno Bienfait, Oliver Sacher, Johann Gasteiger, Roland J Siezen, Arjen Nauta, and Jan M W Geurts

Subjects
Medicine, Science
Abstract

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.

Published
2014
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3. Chemoinformatics: Achievements and Challenges, a Personal View

Author

Johann Gasteiger

Subjects
chemoinformatics, chemical structure representation, chemical databases, data quality, inductive learning, data mining methods, property prediction, QSAR, QSPR, CASE, CASD, Organic chemistry, QD241-441
Abstract

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.

Published
2016
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7. Chemoinformatics and Artificial Intelligence Colloquium: Progress and Challenges to Develop Bioactive Compounds

Author

Jürgen Bajorath, Ana L. Chávez-Hernández, Miquel Duran-Frigola, Eli Fernández-de Gortari, Johann Gasteiger, Edgar López-López, Gerald M. Maggiora, José L. Medina-Franco, Oscar Méndez-Lucio, Jordi Mestres, Ramón Alain Miranda-Quintana, Tudor I. Oprea, Fabien Plisson, Fernando D. Prieto-Martínez, Raquel Rodríguez-Pérez, Paola Rondón-Villarreal, Fernanda I. Saldívar-Gonzalez, Norberto Sánchez-Cruz, and Marilia Valli

Abstract

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/

Published
2022
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9. Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information.

Author

Iurii Sushko, Sergii Novotarskyi, Robert Körner, Anil Kumar Pandey, Matthias Rupp, Wolfram Teetz, Stefan Brandmaier, Ahmed Abdelaziz, Volodymyr V. Prokopenko, Vsevolod Yu. Tanchuk, Roberto Todeschini, Alexandre Varnek, Gilles Marcou, Peter Ertl, Vladimir Potemkin 0001, Maria A. Grishina, Johann Gasteiger, Christof H. Schwab, Igor I. Baskin, Vladimir A. Palyulin, Eugene V. Radchenko, William J. Welsh, Vladyslav V. Kholodovych, Dmitriy Chekmarev, Artem Cherkasov, João Aires-de-Sousa, Qing-You Zhang, Andreas Bender 0002, Florian Nigsch, Luc Patiny, Antony J. Williams, Valery Tkachenko, and Igor V. Tetko

Published
2011
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45. Scoring Ligand Efficiency: Potency, Ligand Efficiency and Product Ligand Efficiency within Big Data Landscape

Author

Roksana Duszkiewicz, Jaroslaw Polanski, Johann Gasteiger, and Anna Pedrys

Subjects
0303 health sciences, Ligand efficiency, Chemistry, business.industry, Big data, Pharmaceutical Science, 01 natural sciences, Combinatorial chemistry, 0104 chemical sciences, 010404 medicinal & biomolecular chemistry, 03 medical and health sciences, Product (mathematics), Drug Discovery, Molecular Medicine, Potency, business, 030304 developmental biology
Abstract

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 pAC50 value with HAC, multiplicative or Product Ligand Efficiency (PLE) was designed and tested. Results: In particular PLE was compared with pAC50 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).

Published
2019
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46. Scoring ligand efficiency

Author

Anna Pedrys, Johann Gasteiger, Jaroslaw Polanski, and Roksana Duszkiewicz

Subjects
Pharmacology, PubChem, Ligand efficiency, ligand efficiency, drug design, Chemistry, heavy atom count, activity, product ligand efficiency, ChEMBL, Pharmaceutical Science, Combinatorial chemistry, drugs
Abstract

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.

Published
2019
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