データ駆動型サイエンス創造センター特別講演会
この度、データ駆動型サイエンス創造センター（DSC）の招聘により、スイス連邦工科大学（ETH)の副学長であるGisbert Schneider教授がご来学されます。
これに合わせて以下の通り特別講演会を開催します。
Gisbert Schneider教授はETHが主催するAIの研究プロジェクトであるRETHINKの責任者として世界的にご活躍されています。
ご興味を持たれた本学教職員および学生の皆さまはこの機会に是非ご参加ください。
開催日時
2023年4月19日（水）13：30~15：00
開催場所
学際融合領域研究棟2号館1階 研修ホール
参加登録
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参加登録期限
令和5年4月18日 12:00まで
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主催
奈良先端科学技術大学院大学 データ駆動型サイエンス創造センター
【問合せ先】
奈良先端科学技術大学院大学 データ駆動型サイエンス創造センター
e-mail: dsc-info[at] dsc.naist.jp
*[at] は ＠ に置きかえてください
TEL: 0743-72-6056
講演の詳細
講演者：
Gisbert Schneider 教授
ETH Zurich, Department of Chemistry and Applied Biosciences, Zurich, Switzerland; ETH Singapore SEC Ltd, Singapore.
Title:
DE NOVO MOLECULAR DESIGN WITH MACHINE INTELLIGENCE
ABSTRACT:
Molecular design may be regarded as a pattern recognition process. Chemists are skilled in visual chemical structure recognition and their association with (retro)synthesis routes and molecular properties. In this context, various “artificial intelligence” (AI) methods have emerged as potentially enabling technology for drug discovery and automation, because these systems aim to mimic the chemist’s pattern recognition process and take it to the next level by considering the available domain–specific data and associations during model development. The same is true for predicting the pharmacological activity and other properties of small molecules. Here, AI technology, in particular deep networks, and jury methods, have advanced the field by providing increasingly accurate qualitative and quantitative prediction models. Part of the appeal of applying AI methods in drug design lies in the potential to develop data-driven, implicit model building processes to navigate vast datasets and to prioritize alternatives. This concept represents at least a partial transfer of decision power to an AI and could be viewed as synergistic with human intelligence; that is, a domain-specific implicit AI that would not only imitate but augment the capabilities of chemists in molecular design and selection. More ambitiously, the ultimate challenge for drug design with AI is to autonomously generate new chemical entities with the desired properties from scratch (de novo), without the need for the often prohibitively costly experimental compound screening.
We will review the principles of AI methods for de novo drug design, emphasizing approaches that have proven useful and reliable in “little-data” scenarios. Selected prospective case studies will be presented, ranging from targeted molecular design to fully automated design-make-test-analyse cycles. We provide a critical assessment of the possibilities and limitations of the individual approaches and dare forecasting the future of drug design with AI.
Selected references:
Moret, M., Pachon Angona, I., Cotos, L., Yan, S., Atz, K., Brunner, C., Baumgartner, M., Grisoni, F., Schneider, G. (2023) Leveraging molecular structure and bioactivity with chemical language models for drug design. Nature Communications 14, 114.
Atz, K., Isert, C., Böcker, N. M. A., Jiménez-Luna, J., Schneider, G. (2022) Δ-Quantum machine learning for medicinal chemistry. Physical Chemistry Chemical Physics 24, 10775-10783
Grisoni, F., Huisman, B., Button, A., Moret, M., Atz, K., Merk, D., Schneider, G. (2021) Combining generative artificial intelligence and on-chip synthesis for de novo drug design. Science Advances 7, eabg3338.
Friedrich, L., Cingolani, G., Ko, Y.-H., Iaselli, M., Miciaccia, M., Perrone, M. G., Neukirch, K., Bobinger, V., Merk, D., Hofstetter, R. K., Werz, O., Koeberle, A., Scilimati, A., Schneider, G. (2021) Learning from nature: From a marine natural product to synthetic cyclooxygenase-1 inhibitors by automated de novo design. Advanced Science 8, 2100832.
Schneider, G. (2018) Automating drug discovery. Nature Reviews Drug Discovery 17, 97–113.
