Fragment-Masked Molecular Optimization
<|reference_start|>Fragment-Masked Molecular Optimization: Molecular optimization is a crucial aspect of drug discovery, aimed at refining molecular structures to enhance drug efficacy and minimize side effects, ultimately accelerating the overall drug development process. Many target-based molecular optimization methods have been proposed, significantly advancing drug discovery. These methods primarily on understanding the specific drug target structures or their hypothesized roles in combating diseases. However, challenges such as a limited number of available targets and a difficulty capturing clear structures hinder innovative drug development. In contrast, phenotypic drug discovery (PDD) does not depend on clear target structures and can identify hits with novel and unbiased polypharmacology signatures. As a result, PDD-based molecular optimization can reduce potential safety risks while optimizing phenotypic activity, thereby increasing the likelihood of clinical success. Therefore, we propose a fragment-masked molecular optimization method based on PDD (FMOP). FMOP employs a regression-free diffusion model to conditionally optimize the molecular masked regions without training, effectively generating new molecules with similar scaffolds. On the large-scale drug response dataset GDSCv2, we optimize the potential molecules across all 945 cell lines. The overall experiments demonstrate that the in-silico optimization success rate reaches 94.4%, with an average efficacy increase of 5.3%. Additionally, we conduct extensive ablation and visualization experiments, confirming that FMOP is an effective and robust molecular optimization method. The code is available at:https://anonymous.4open.science/r/FMOP-98C2.<|reference_end|>
@article{li2024fragment-masked,
    title={Fragment-Masked Molecular Optimization},
    author={Kun Li, Xiantao Cai, Jia Wu, Bo Du, Wenbin Hu},
    journal={arXiv preprint arXiv:2408.09106},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.09106},
    primaryClass={q-bio.BM cs.AI}
}