Ramil Nugmanov
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View article: Benchmarking molecular conformer augmentation with context-enriched training: graph-based transformer versus GNN models
Benchmarking molecular conformer augmentation with context-enriched training: graph-based transformer versus GNN models Open
The field of molecular representation has witnessed a shift towards models trained on molecular structures represented by strings or graphs, with chemical information encoded in nodes and bonds. Graph-based representations offer a more rea…
View article: Pretraining Graph Transformers with Atom-in-a-Molecule Quantum Properties for Improved ADMET Modeling
Pretraining Graph Transformers with Atom-in-a-Molecule Quantum Properties for Improved ADMET Modeling Open
We evaluate the impact of pretraining Graph Transformer architectures on atom-level quantum-mechanical features for the modeling of absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of drug-like compounds. We…
View article: Atom-Level Quantum Pretraining Enhances the Spectral Perception of Molecular Graphs in Graphormer
Atom-Level Quantum Pretraining Enhances the Spectral Perception of Molecular Graphs in Graphormer Open
This study explores the impact of pretraining Graph Transformers using atom-level quantum-mechanical features for molecular property modeling. We utilize the ADMET Therapeutic Data Commons datasets to evaluate the benefits of this approach…
View article: Analysis of Atom-level pretraining with Quantum Mechanics (QM) data for Graph Neural Networks Molecular property models
Analysis of Atom-level pretraining with Quantum Mechanics (QM) data for Graph Neural Networks Molecular property models Open
Despite the rapid and significant advancements in deep learning for Quantitative Structure-Activity Relationship (QSAR) models, the challenge of learning robust molecular representations that effectively generalize in real-world scenarios …
View article: ReacLLaMA: Merging chemical and textual information in chemical reactivity AI models
ReacLLaMA: Merging chemical and textual information in chemical reactivity AI models Open
Chemical reactivity models are developed to predict chemical reaction outcomes in the form of classification (success/failure) or regression (product yield) tasks. The vast majority of the reported models are trained solely on chemical inf…
View article: CCDC 2262516: Experimental Crystal Structure Determination
CCDC 2262516: Experimental Crystal Structure Determination Open
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available …
View article: CCDC 2262514: Experimental Crystal Structure Determination
CCDC 2262514: Experimental Crystal Structure Determination Open
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available …
View article: CCDC 2262517: Experimental Crystal Structure Determination
CCDC 2262517: Experimental Crystal Structure Determination Open
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available …
View article: CCDC 2262515: Experimental Crystal Structure Determination
CCDC 2262515: Experimental Crystal Structure Determination Open
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available …
View article: CCDC 2262513: Experimental Crystal Structure Determination
CCDC 2262513: Experimental Crystal Structure Determination Open
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available …
View article: Global reactivity models are impactful in industrial synthesis applications
Global reactivity models are impactful in industrial synthesis applications Open
Artificial Intelligence is revolutionizing many aspects of the pharmaceutical industry. Deep learning models are now routinely applied to guide drug discovery projects leading to faster and improved findings, but there are still many tasks…
View article: Oxyethylated Fluoresceine—(thia)calix[4]arene Conjugates: Synthesis and Visible-Light Photoredox Catalysis in Water–Organic Media
Oxyethylated Fluoresceine—(thia)calix[4]arene Conjugates: Synthesis and Visible-Light Photoredox Catalysis in Water–Organic Media Open
Fluorescent derivatives attract the attention of researchers for their use as sensors, photocatalysts and for the creation of functional materials. In order to create amphiphilic fluorescent derivatives of calixarenes, a fluorescein deriva…
View article: Global Reactivity Models are Impactful in Industrial Synthesis Applications
Global Reactivity Models are Impactful in Industrial Synthesis Applications Open
Artificial Intelligence is revolutionizing many aspects of the pharmaceutical industry. Deep learning models are now routinely applied to guide drug discovery projects leading to faster and improved findings, but there are still many tasks…
View article: Global Reactivity Models are Impactful in Industrial Synthesis Applications
Global Reactivity Models are Impactful in Industrial Synthesis Applications Open
Artificial Intelligence is revolutionizing many aspects of the pharmaceutical industry. Deep learning models are now routinely applied to guide drug discovery projects leading to faster and improved findings, but there are still many tasks…
View article: Bidirectional Graphormer for Reactivity Understanding: Neural Network Trained to Reaction Atom-to-Atom Mapping Task
Bidirectional Graphormer for Reactivity Understanding: Neural Network Trained to Reaction Atom-to-Atom Mapping Task Open
This work introduces GraphormerMapper, a new algorithm for reaction atom-to-atom mapping (AAM) based on a transformer neural network adopted for the direct processing of molecular graphs as sets of atoms and bonds, as opposed to SMILES/SEL…
View article: Bidirectional Graphormer for Reactivity Understanding: neural network trained to reaction atom-to-atom mapping task
Bidirectional Graphormer for Reactivity Understanding: neural network trained to reaction atom-to-atom mapping task Open
This work introduces GraphormerMapper – a new algorithm for reactions atom-to-atom mapping (AAM) based on a distance-aware BERT neural network. In benchmarking studies with IBM RxnMapper, the best AAM algorithm according to our previous st…
View article: Prediction of Optimal Conditions of Hydrogenation Reaction Using the Likelihood Ranking Approach
Prediction of Optimal Conditions of Hydrogenation Reaction Using the Likelihood Ranking Approach Open
The selection of experimental conditions leading to a reasonable yield is an important and essential element for the automated development of a synthesis plan and the subsequent synthesis of the target compound. The classical QSPR approach…
View article: Atom‐to‐atom Mapping: A Benchmarking Study of Popular Mapping Algorithms and Consensus Strategies
Atom‐to‐atom Mapping: A Benchmarking Study of Popular Mapping Algorithms and Consensus Strategies Open
In this paper, we compare the most popular Atom‐to‐Atom Mapping (AAM) tools: ChemAxon, [1] Indigo, [2] RDTool, [3] NameRXN (NextMove), [4] and RXNMapper [5] which implement different AAM algorithms. An open‐source RDTool program was optimi…
View article: Azocalix[4]arene-Rhodamine Supramolecular Hypoxia-Sensitive Systems: A Search for the Best Calixarene Hosts and Rhodamine Guests
Azocalix[4]arene-Rhodamine Supramolecular Hypoxia-Sensitive Systems: A Search for the Best Calixarene Hosts and Rhodamine Guests Open
A potential hypoxia-sensitive system host-guest complex of three calixarenes (including two with four anionic carboxyl and sulphonate azo fragments on the upper rim and a newly synthesized bis-azo adduct of calixarene in the cone configura…
View article: Reaction Data Curation I: Chemical Structures and Transformations Standardization
Reaction Data Curation I: Chemical Structures and Transformations Standardization Open
The quality of experimental data for chemical reactions is a critical consideration for any reaction‐driven study. However, the curation of reaction data has not been extensively discussed in the literature so far. Here, we suggest a 4 ste…
View article: Switching Ion Binding Selectivity of Thiacalix[4]arene Monocrowns at Liquid–Liquid and 2D-Confined Interfaces
Switching Ion Binding Selectivity of Thiacalix[4]arene Monocrowns at Liquid–Liquid and 2D-Confined Interfaces Open
Understanding the interaction of ions with organic receptors in confined space is of fundamental importance and could advance nanoelectronics and sensor design. In this work, metal ion complexation of conformationally varied thiacalix[4]mo…
View article: Cross-validation strategies in QSPR modelling of chemical reactions
Cross-validation strategies in QSPR modelling of chemical reactions Open
In this article, we consider cross-validation of the quantitative structure-property relationship models for reactions and show that the conventional k-fold cross-validation (CV) procedure gives an 'optimistically' biased assessment of pre…
View article: Combined Graph/Relational Database Management System for Calculated Chemical Reaction Pathway Data
Combined Graph/Relational Database Management System for Calculated Chemical Reaction Pathway Data Open
Presently, quantum chemical calculations are widely used to generate extensive data sets for machine learning applications; however, generally, these sets only include information on equilibrium structures and some close conformers. Explor…
View article: QSAR Modeling Based on Conformation Ensembles Using a Multi-Instance Learning Approach
QSAR Modeling Based on Conformation Ensembles Using a Multi-Instance Learning Approach Open
Modern QSAR approaches have wide practical applications in drug discovery for screening potentially bioactive molecules before their experimental testing. Most models predicting the bioactivity of compounds are based on molecular descripto…
View article: QSAR Modeling Based on Conformation Ensembles Using a Multi-Instance Learning Approach
QSAR Modeling Based on Conformation Ensembles Using a Multi-Instance Learning Approach Open
Modern QSAR approaches have wide practical applications in drug discovery for screening potentially bioactive molecules before their experimental testing. Most models predicting the bioactivity of compounds are based on molecular descripto…
View article: Combined Graph/relational Database Management System for Calculated Chemical Reaction Pathway Data
Combined Graph/relational Database Management System for Calculated Chemical Reaction Pathway Data Open
Nowadays quantum chemical calculations are widely used to generate extensive datasets for machine learning applications, however, generally these sets only include information on equilibrium structures and some close conformers. Exploratio…
View article: Combined Graph/relational Database Management System for Calculated Chemical Reaction Pathway Data
Combined Graph/relational Database Management System for Calculated Chemical Reaction Pathway Data Open
Nowadays quantum chemical calculations are widely used to generate extensive datasets for machine learning applications, however, generally these sets only include information on equilibrium structures and some close conformers. Exploratio…