Valery Tkachenko

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Valery Tkachenko graduated from the Lomonosov Moscow State University. He is currently Data Science Manager at the Royal Society of Chemistry. Over the course of the last 20 years he has developed many successful enterprise projects for large pharmaceutical companies and the public domain, including PubChem and ChemSpider. With the Royal Society of Chemistry Valery is building a new type of information systems connecting people and resources into a distributed heterogeneous semantic network facilitating global scientific research. Valery is the author of more than 20 peer reviewed papers and book chapters.

chemistry data repository machine learning chemistry database artificial intelligence chemistry cheminformatics semantic web qsar data repository chemspider deep learning chemical database openphacts de-novo design qspr toxicology chemical data chemistry validation osdr open science chemical validation global chemistry network inchi microservices complexation extraction f-elements in-silico design materials design metal-organic framework mof pubmed natural language processing nlp doc2vec word2vec text mining research data drug discovery gpu nvidia api government databases admet fair data chemical registry hl7 spl chemical format nanoregistry materials nanomaterialsregistry nanomaterials comptox cvsp chemistry standardization chemistry standards visualization chemical space reactions database chemical reactions ontologies linked data federated databases nosql clustering cheminformatics; open source cloud computing

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Valery Tkachenko

Evolution of public chemistry databases: past and the future

In silico design of new functional materials

Metal-organic frameworks: from database to supramolecular effects in complexation

Abstract recommendation system: beyond word-level representations

Machine learning methods for chemical properties and toxicity based endpoints

Chemical workflows supporting automated research data collection

Deep learning methods applied to physicochemical and toxicological endpoints

Deep Learning on nVidia GPUs for QSAR, QSPR and QNAR predictions

Using publicly available resources to build a comprehensive knowledgebase of chemical information

Need and benefits for structure standardization to facilitate integration and connectivity between government databases

Development and comparison of deep learning toolkit with other machine learning methods

Living in a world of federated knowledge challenges, principles, tools and solutions

Open chemistry registry and mapping platform based on open source cheminformatics toolkits

Using the structured product labeling format to index versatile chemical data

Tools and approaches for data deposition into nanomaterial databases

Chemistry Validation and Standardization Platform v2.0

Open Science Data Repository - the platform for materials research

Opportunities in chemical structure standardization

OpenPHACTS - Chemistry Platform Update and Learnings

Evolution of open chemical information