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Showing 1–8 of 8 results

Advanced filters: Author: Daniil A. Boiko Clear advanced filters

Advancing molecular machine learning representations with stereoelectronics-infused molecular graphs

Boiko et al. enhance expressiveness and interpretability of molecular representation in graph neural networks by including quantum-chemical-rich information into molecular graphs.

Daniil A. Boiko
Thiago Reschützegger
Gabe Gomes
Research23 May 2025
Nature Machine Intelligence

Volume: 7, P: 771-781
Discovering organic reactions with a machine-learning-powered deciphering of tera-scale mass spectrometry data

Mass spectrometry generates vast amounts of data in chemistry labs. Here, authors developed a machine learning-driven search engine that analyzes archived data to discover chemical reactions without performing additional experiments.

Konstantin S. Kozlov
Daniil A. Boiko
Valentine P. Ananikov
ResearchOpen Access16 Mar 2025
Nature Communications

Volume: 16, P: 1-12
Connecting chemical and protein sequence space to predict biocatalytic reactions

A two-phase machine-learning-based tool making use of high-throughput experimentation is introduced to examine the connections between chemical and protein sequence space and predict productive biocatalytic reactions among substrate and enzyme pairs.

Alexandra E. Paton
Daniil A. Boiko
Alison R. H. Narayan
ResearchOpen Access01 Oct 2025
Nature

Volume: 646, P: 108-116
Autonomous chemical research with large language models

Coscientist is an artificial intelligence system driven by GPT-4 that autonomously designs, plans and performs experiments by incorporating large language models empowered by tools such as internet and documentation search, code execution and experimental automation.

Daniil A. Boiko
Robert MacKnight
Gabe Gomes
ResearchOpen Access20 Dec 2023
Nature

Volume: 624, P: 570-578
Deep generative modeling of annotated bacterial biofilm images

Angelina A. Holicheva
Konstantin S. Kozlov
Valentine P. Ananikov
ResearchOpen Access14 Jan 2025
npj Biofilms and Microbiomes

Volume: 11, P: 1-15
Rethinking chemical research in the age of large language models

This Perspective highlights the potential integrations of large language models (LLMs) in chemical research and provides guidance on the effective use of LLMs as research partners, noting the ethical and performance-based challenges that must be addressed moving forward.

Robert MacKnight
Daniil A. Boiko
Gabe Gomes
Reviews24 Jun 2025
Nature Computational Science

Volume: 5, P: 715-726
Integration of thermal imaging and neural networks for mechanical strength analysis and fracture prediction in 3D-printed plastic parts

Daniil A. Boiko
Victoria A. Korabelnikova
Valentine P. Ananikov
ResearchOpen Access27 May 2022
Scientific Reports

Volume: 12, P: 1-15
Electron microscopy dataset for the recognition of nanoscale ordering effects and location of nanoparticles

Daniil A. Boiko
Evgeniy O. Pentsak
Valentine P. Ananikov
ResearchOpen Access25 Mar 2020
Scientific Data

Volume: 7, P: 1-8