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Open Data and Algorithms in AI-driven molecular informatics
- Data sharing is crucial for AI models for molecular informatics. Open data, open-source software, and open science are steps to solve the problem of data scarcity.
- Open data frameworks will facilitate the use of AI in almost every sub-domain of chemistry, and the introduction of open science and data sharing helps AI-enhanced molecular informatics take over a leading role in its digital evolution.
- Germany’s NFDI supports FAIR principles by making chemical data available to AI applications, and open repositories like NFDI4Chem project, nmrXiv provide valuable resources in AI-driven chemistry.
- Sharing data through databases like the Protein Data Bank (PDB) and the Cambridge Crystallographic Database (CCD) helps in SARS-CoV-2 research and increases research capacity by assisting with activities like drug candidate identification and natural product classification.
- Open-source chemical informatics libraries like RDKit, CDK, and OpenBabel provide the necessary tools for processing and analyzing chemical data.
- AI models are better at analyzing chemical structures thanks to advances in chemical string representations such as DeepSMILES and SELFIES, which reduce the rate of invalid outputs compared to previous representations.
- Digitalization of synthetic chemistry depends on the experimental data available through machine learning applications, which can enhance yield prediction in chemical processes.
- AI-based chemical application development is heavily reliant on text extraction techniques, which transform previously unusable data into usable formats.
- Open access to resources such as MetaboLights and the Human Metabolome Database aids in identification of bioactive compounds and integration of genome and metabolome data.
- Further development of open science and data sharing can accelerate AI-enhanced molecular informatics in the digital evolution of chemistry, driving innovation and research for the future.
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