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    Protein-Ligand Binding Affinity Directed Multi-Objective Drug Design Based on Fragment Representation Methods

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    Author
    Mukaidaisi, Muhetaer
    Keyword
    drug design
    multi-objective optimization
    deep evolutionary learning
    graph fragmentation
    protein-ligand binding affinity
    
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    URI
    http://hdl.handle.net/10464/17426
    Abstract
    Drug discovery is a challenging process with a vast molecular space to be explored and numerous pharmacological properties to be appropriately considered. Among various drug design protocols, fragment-based drug design is an effective way of constraining the search space and better utilizing biologically active compounds. Motivated by fragment-based drug search for a given protein target and the emergence of artificial intelligence (AI) approaches in this field, this work advances the field of in silico drug design by (1) integrating a graph fragmentation-based deep generative model with a deep evolutionary learning process for large-scale multi-objective molecular optimization, and (2) applying protein-ligand binding affinity scores together with other desired physicochemical properties as objectives. Our experiments show that the proposed method can generate novel molecules with improved property values and binding affinities.
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