Study of Evolutionary Algorithms for Multi-objective Optimization

Gaikwad, Rama; Lakshmanan, L.

doi:10.1007/s42979-022-01283-x

Cited by 2 publications

(1 citation statement)

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“…Firstly, the complexity of concurrently optimizing multiple molecular properties renders single-objective optimization via weighted summation inadequate for fully addressing the original problem, thereby constraining the search space for multi-property optimization 41 . Secondly, determining the relative importance of multiple molecular properties through weighting is challenging and often requires experimental adjustment 42 .…”

Section: Introductionmentioning

confidence: 99%

Evolutionary multi-objective molecule optimization in implicit chemical space

Xia

Zheng

et al. 2023

Preprint

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Optimization techniques play a pivotal role in advancing molecular optimization, prompting the development of numerous generative methods tailored to efficiently design optimized molecules derived from existing lead compounds. However, these methodologies often encounter difficulties in generating diverse, novel, and high-quality molecules when addressing multi-property tasks. Consequently, efficiently searching for diverse optimized candidates that simultaneously satisfy multiple properties remains a significant challenge in molecule optimization. To address this problem, we propose a multi-objective molecule optimization framework (MOMO). MOMO employs a specially designed Pareto dominance-based multi-property evaluation strategy at the molecular sequence level, specifically designed to guide the evolutionary search in a latent molecular space to optimize multiple molecular properties. A comparative analysis of MOMO with extant state-of-the-art baselines across three multi-property molecule optimization tasks reveals that MOMO markedly outperforms them all. These results suggest the efficacy of the proposed MOMO framework for simultaneous optimization of multiple properties in molecule optimization.

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Section: Introductionmentioning

confidence: 99%