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Artificial Intelligence Generative Chemistry Design of Target-Specific Scaffold-Focused Small Molecule Drug Libraries

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Abstract

The de novo design of scaffold-focused and target-specific molecular structures using deep learning generative modeling introduces a promising solution to the discovery of novel and potent bioactive drug compounds. Deep learning generative modeling exhibits the creativity that machine intelligence can offer in composing, painting, and even the scratching of novel molecular structures. This chapter mainly covers that how generative chemistry can be effectively applied to the design and generation of scaffold-focused and target-specific small molecules. To this emerging paradigm, the chapter starts with a brief history of artificial intelligence (AI) in drug discovery. Chemical databases, molecular representations, and cheminformatics related tools are covered as the infrastructure. Two example applications of using generative adversarial networks (GAN) and recurrent neural networks (RNN) to realize the de novo compound generation towards the cannabinoid receptor 2 (CB2) are discussed in the chapter. Summary, challenges, and future perspectives follow.

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Acknowledgments

The authors acknowledge the funding support to the **e Laboratory and CDAR Center from the NIH (R01DA052329, P30PDA035778A and R56AG074951).

Author information

Authors and Affiliations

  1. Center for the Development of Therapeutics, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Yuemin Bian

  2. Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, Pharmacometrics and System Pharmacology (PSP) PharmacoAnalytics Program, School of Pharmacy, Pittsburgh, PA, USA

    Yuemin Bian, Gavin Hou & **ang-Qun **e

  3. National Center of Excellence for Computational Drug Abuse Research, Pittsburgh, PA, USA

    Gavin Hou & **ang-Qun **e

  4. Drug Discovery Institute, Pittsburgh, PA, USA

    **ang-Qun **e

  5. Departments of Computational Biology and Structural Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA

    **ang-Qun **e

Authors
  1. Yuemin Bian
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  2. Gavin Hou
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  3. **ang-Qun **e
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Corresponding author

Correspondence to **ang-Qun **e .

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Editors and Affiliations

  1. Retired Senior Expert Pharmacologist at the Office of Cardiology, Hematology, Endocrinology, and Nephrology, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA

    Gowraganahalli Jagadeesh

  2. Professor & Director, Research Training and Publications, The Office of Research and Development, Periyar Maniammai Institute of Science & Technology (Deemed to be University), Vallam, Tamil Nadu, India

    Pitchai Balakumar

  3. Division Cardiology & Nephrology, Office of Cardiology, Hematology, Endocrinology and Nephrology, Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, MD, USA

    Fortunato Senatore

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Bian, Y., Hou, G., **e, XQ. (2023). Artificial Intelligence Generative Chemistry Design of Target-Specific Scaffold-Focused Small Molecule Drug Libraries. In: Jagadeesh, G., Balakumar, P., Senatore, F. (eds) The Quintessence of Basic and Clinical Research and Scientific Publishing. Springer, Singapore. https://doi.org/10.1007/978-981-99-1284-1_31

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