Automatized Assessment of Protective Group Reactivity: A Step Toward Big Reaction Data Analysis. - Arkadii I. Lin; Timur I. Madzhidov; Olga Klimchuk; Ramil I. Nugmanov; Igor S. Antipin; Alexandre Varnek (J. Chem. Inf. Model., 2016)

Structure-reactivity modeling using mixture-based representation of chemical reactions. - Pavel G. Polishchuk; Timur I. Madzhidov; Timur Gimadiev; Andrey Bodrov; Ramil I. Nugmanov; Alexandre Varnek (J. Comput. Aided Mol. Des., 2017)

Assessment of tautomer distribution using the condensed reaction graph approach. - Timur Gimadiev; Timur I. Madzhidov; Ramil I. Nugmanov; Igor I. Baskin; Igor S. Antipin; Alexandre Varnek (J. Comput. Aided Mol. Des., 2018)

CGRtools: Python Library for Molecule, Reaction, and Condensed Graph of Reaction Processing. - Ramil I. Nugmanov; Ravil N. Mukhametgaleev; Tagir Akhmetshin; Timur Gimadiev; Valentina A. Afonina; Timur I. Madzhidov; Alexandre Varnek (J. Chem. Inf. Model., 2019)

Conjugated Quantitative Structure-Property Relationship Models: Application to Simultaneous Prediction of Tautomeric Equilibrium Constants and Acidity of Molecules. - Dmitry V. Zankov; Timur I. Madzhidov; Asima I. Rakhimbekova; Timur Gimadiev; Ramil I. Nugmanov; Marina A. Kazymova; Igor I. Baskin; Alexandre Varnek (J. Chem. Inf. Model., 2019)

Combined Graph/Relational Database Management System for Calculated Chemical Reaction Pathway Data. - Timur Gimadiev; Ramil I. Nugmanov; Dinar Batyrshin; Timur I. Madzhidov; Satoshi Maeda; Pavel Sidorov; Alexandre Varnek (J. Chem. Inf. Model., 2021)

QSAR Modeling Based on Conformation Ensembles Using a Multi-Instance Learning Approach. - Dmitry V. Zankov; Mariia Matveieva; Aleksandra V. Nikonenko; Ramil I. Nugmanov; Igor I. Baskin; Alexandre Varnek; Pavel G. Polishchuk; Timur I. Madzhidov (J. Chem. Inf. Model., 2021)

CGRdb2.0: A Python Database Management System for Molecules, Reactions, and Chemical Data. - Timur Gimadiev; Ramil I. Nugmanov; Aigul Khakimova; Adeliya Fatykhova; Timur I. Madzhidov; Pavel Sidorov; Alexandre Varnek (J. Chem. Inf. Model., 2022)

Bidirectional Graphormer for Reactivity Understanding: Neural Network Trained to Reaction Atom-to-Atom Mapping Task. - Ramil I. Nugmanov; Natalia Dyubankova; Andrey Gedich; Jörg Kurt Wegner (J. Chem. Inf. Model., 2022)

Global reactivity models are impactful in industrial synthesis applications. - Paulo Neves; Kelly McClure; Jonas Verhoeven; Natalia Dyubankova; Ramil I. Nugmanov; Andrey Gedich; Sairam Menon; Zhicai Shi; Jörg K. Wegner (J. Cheminformatics, 2023)

Correction: Global reactivity models are impactful in industrial synthesis applications. - Paulo Neves; Kelly McClure; Jonas Verhoeven; Natalia Dyubankova; Ramil I. Nugmanov; Andrey Gedich; Sairam Menon; Zhicai Shi; Jörg K. Wegner (J. Cheminformatics, 2023)

Atom-Level Quantum Pretraining Enhances the Spectral Perception of Molecular Graphs in Graphormer. - Alessio Fallani; José Antonio Arjona-Medina; Konstantin Chernichenko; Ramil I. Nugmanov; Jörg Kurt Wegner; Alexandre Tkatchenko (AIDD@ICANN, 2024)

ReacLLaMA: Merging chemical and textual information in chemical reactivity AI models. - Aline Hartgers; Ramil I. Nugmanov; Kostiantyn Chernichenko; Jörg Kurt Wegner (CoRR, 2024)

Analysis of Atom-level pretraining with Quantum Mechanics (QM) data for Graph Neural Networks Molecular property models. - Jose A. Arjona-Medina; Ramil I. Nugmanov (CoRR, 2024)

PaCh (Packed Chemicals): Computationally Effective Binary Format for Chemical Structure Encoding. - Ramil I. Nugmanov (J. Chem. Inf. Model., 2024)

Pretraining graph transformers with atom-in-a-molecule quantum properties for improved ADMET modeling. - Alessio Fallani; Ramil I. Nugmanov; Jose A. Arjona-Medina; Jörg Kurt Wegner; Alexandre Tkatchenko; Kostiantyn Chernichenko (J. Cheminformatics, 2025)

Benchmarking molecular conformer augmentation with context-enriched training: graph-based transformer versus GNN models. - Cecile Valsecchi; Jose A. Arjona-Medina; Natalia Dyubankova; Ramil I. Nugmanov (J. Cheminformatics, 2025)