Ramil I. Nugmanov

According to our database, Ramil I. Nugmanov authored at least 16 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

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

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

ReacLLaMA: Merging chemical and textual information in chemical reactivity AI models.

Aline Hartgers, Ramil I. Nugmanov, Kostiantyn Chernichenko, Jörg Kurt Wegner

CoRR, 2024

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

Proceedings of the AI in Drug Discovery - First International Workshop, 2024

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, December, 2023

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, December, 2023

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

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

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

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

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

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

2018

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

2017

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

2016

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