Table 1 Program of the chemoinformatics and artificial intelligence colloquium and related links

Johann Gasteiger

University of Erlangen- Nuremberg (Germany)

Chemistry in times of artificial intelligence

[4,5,6,7]

Marilia Valli

University of São Paulo (Brazil)

Brazilian biodiversity chemical space into NuBBE database

[8]

Fernando Prieto D. Prieto-Martínez

National Autonomous University of México (Mexico)

A bird’s eye view of AI in structure-based drug design

[9,10,11]

Paola Rondón-Villarreal

Industrial University of Santander. Currently Universidad de Santander (Colombia)

Machine learning in virtual screening and peptide’s design

[12]

Fabien Plisson

Center for Research and Advanced Studies of the National Polytechnic Institute (CINVESTAV-IPN) (Mexico)

Probing the limits in AI-driven peptide design

[13]

Miquel Duran-Frigola

Ersilia Open Source Initiative (UK)

Ersilia, a hub of AI/ML models for infectious and neglected tropical diseases

[14, 15]

Eli Fernández-de Gortari

International Iberian Nanotechnology Laboratory (INL) (Portugal)

The role of generated chemical space in ML-based virtual screening

[16,17,18]

Norberto Sánchez-Cruz

Chemotargets, LLC (Spain); National Autonomous University of México (Mexico)

Deep graph learning for protein-fragment binding predictions

[19]

Raquel Rodríguez-Pérez

Novartis (Switzerland)

Machine learning for the prediction of ADME properties in pharmaceutical industry

[20, 21]

Jordi Mestres

Chemotargets, LLC (Spain)

Challenges and benefits of integrating the preclinical-to-postmarketing safety data continuum

[19]

Gerald M. Maggiora

University of Arizona (USA)

Development of a soft rule of five

[22]

Ramón A. Miranda-Quintana

University of Florida (USA)

Extended similarity analysis: from pair of molecules, to chemical space and beyond

[23, 24]

Jürgen Bajorath

University of Bonn (Germany)

DeepSARM: From structural and SAR analysis to compound design and optimization

[25, 26]

Oscar Méndez-Lucio

Recursion Pharmaceuticals (USA)

Geometric deep learning for structure-based drug design

[27]

Tudor I. Oprea

Roivant Sciences (USA)

Learning from machine learning: some lessons from a gene-centric Alzheimer’s model

[28, 29]