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Workshop
Charting Chemical Space: Challenges and Opportunities for AI and Machine Learning
Pierre Baldi · Klaus-Robert Müller · Gisbert Schneider

Sat Dec 11 07:30 AM -- 06:30 PM (PST) @ Westin: Glacier

In spite of its central role and position between physics and biology, chemistry has remained in a somewhat backward state of informatics development compared to its two close relatives, primarily for historical reasons. Computers, open public databases, and large collaborative projects have become the pervasive hallmark of research in physics and biology, but are still at an early stage of development in chemistry. Recently, however, large repositories with millions of small molecules have become freely available, and equally large repositories of chemical reactions have also become available, albeit not freely. These data create a wealth of interesting informatics and machine learning challenges to efficiently store, search, and predict the physical, chemical, and biological properties of small molecules and reactions and chart ``chemical space'', with significant scientific and technological impacts.

Small organic molecules, in particular, with at most a few dozen atoms play a fundamental role in chemistry, biology, biotechnology, and pharmacology. They can be used, for instance, as combinatorial building blocks for chemical synthesis, as molecular probes for perturbing and analyzing biological systems in chemical genomics and systems biology, and for the screening, design, and discovery of new drugs and other useful compounds. Huge arrays of new small molecules can be produced in a relatively short time. Chemoinformatics methods must be able to cope with the inherently graphical, non-vectorial, nature of raw chemical information on small organic molecules and organic reactions, and the vast combinatorial nature of chemical space, containing over 1060 possible small organic molecules. Recently described grand challenges for chemoinformatics include: (1) overcoming stalled drug discovery; (2) helping to develop green chemistry and address global warming; (3) understanding life from a chemical perspective; and (4) enabling the network of the world\'s chemical and biological information to be accessible and interpretable.

This one day workshop will provide a forum to brainstorm about these issues, explore the role and contributions machine learning methods can make to chemistry and chemoinformatics, and hopefully foster new ideas and collaborations.

Author Information

Pierre Baldi (UC Irvine)
Klaus-Robert Müller (TU Berlin)
Gisbert Schneider (ETH)

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