AI in 3D compound design |
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| Institution: | 1. Department of Computer Engineering, Koc University, Istanbul, 34450, Turkey;2. KUIS AI Center, Koc University, Istanbul, 34450, Turkey;3. Graduate School of Science and Engineering, Koc University, Istanbul, 34450, Turkey;4. Department of Chemical and Biological Engineering, Koc University, Istanbul, 34450, Turkey;5. School of Medicine, Koc University, Istanbul, 34450, Turkey;1. Department of Biology, The University of Akron, 302 Buchtel Common, Akron, OH 44325, USA;2. Department of Chemical and Biomolecular Engineering, The University of Akron, 302 Buchtel Common, Akron, OH 44325, USA;1. Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44195, USA;2. Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, VA, 22903, USA;3. Biosciences and Biotechnology Division, Physical and Life Sciences Directorate, Lawrence Livermore National Lab, Livermore, CA, 94550, USA;4. Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, MD, 21702, USA;5. Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv, 69978, Israel;6. Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, 44195, USA;7. Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA;1. Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University, Shanghai, China;2. School of Information Science and Technology, ShanghaiTech University, Shanghai, China;3. Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Pudong, Shanghai, 201203, China |
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| Abstract: | The success of Artificial Intelligence (AI) across a wide range of domains has fuelled significant interest in its application to designing novel compounds and screening compounds against a specific target. However, many existing AI methods either do not account for the 3D structure of the target at all or struggle to capture meaningful spatial information from the target. In this Opinion, we highlight a range of recent structure-aware approaches which utilise deep learning for compound design and virtual screening. We discuss how such methods can be better integrated into existing drug discovery pipelines by facilitating the design of compounds which conform to a specified design hypothesis and by uncovering key protein-ligand interactions which can be used to aid molecule design. |
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