Molecular representations in bio-cheminformatics

IF 3.3 2区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Memetic Computing Pub Date : 2024-07-20 DOI:10.1007/s12293-024-00414-6

Thanh-Hoang Nguyen-Vo, Paul Teesdale-Spittle, Joanne E. Harvey, Binh P. Nguyen

引用次数: 0

Abstract

Molecular representations have essential roles in bio-cheminformatics as they facilitate the growth of machine learning applications in numerous sub-domains of biology and chemistry, especially drug discovery. These representations transform the structural and chemical information of molecules into machine-readable formats that can be efficiently processed by computer programs. In this paper, we present a comprehensive review, providing readers with diverse perspectives on the strengths and weaknesses of well-known molecular representations, along with their respective categories and implementation sources. Moreover, we provide a summary of the applicability of these representations in de novo molecular design, molecular property prediction, and chemical reactions. Besides, representations for macromolecules are discussed with highlighted pros and cons. By addressing these aspects, we aim to offer a valuable resource on the significant role of molecular representations in advancing bio-cheminformatics and its related domains.

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生物化学信息学中的分子表征

分子表征在生物化学信息学中发挥着至关重要的作用，因为它们促进了机器学习应用在生物和化学众多子领域的发展，尤其是药物发现领域。这些表征将分子的结构和化学信息转化为机器可读的格式，以便计算机程序进行高效处理。在本文中，我们将对众所周知的分子表征的优缺点，以及它们各自的类别和实现来源进行全面综述，为读者提供不同的视角。此外，我们还总结了这些表征在全新分子设计、分子性质预测和化学反应中的适用性。此外，我们还讨论了大分子的表征方法，并强调了其利弊。通过对这些方面的讨论，我们旨在提供一份宝贵的资料，说明分子表征在推动生物化学信息学及其相关领域发展方面的重要作用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Memetic Computing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-OPERATIONS RESEARCH & MANAGEMENT SCIENCE

CiteScore

6.80

自引率

12.80%

发文量

期刊介绍： Memes have been defined as basic units of transferrable information that reside in the brain and are propagated across populations through the process of imitation. From an algorithmic point of view, memes have come to be regarded as building-blocks of prior knowledge, expressed in arbitrary computational representations (e.g., local search heuristics, fuzzy rules, neural models, etc.), that have been acquired through experience by a human or machine, and can be imitated (i.e., reused) across problems. The Memetic Computing journal welcomes papers incorporating the aforementioned socio-cultural notion of memes into artificial systems, with particular emphasis on enhancing the efficacy of computational and artificial intelligence techniques for search, optimization, and machine learning through explicit prior knowledge incorporation. The goal of the journal is to thus be an outlet for high quality theoretical and applied research on hybrid, knowledge-driven computational approaches that may be characterized under any of the following categories of memetics: Type 1: General-purpose algorithms integrated with human-crafted heuristics that capture some form of prior domain knowledge; e.g., traditional memetic algorithms hybridizing evolutionary global search with a problem-specific local search. Type 2: Algorithms with the ability to automatically select, adapt, and reuse the most appropriate heuristics from a diverse pool of available choices; e.g., learning a mapping between global search operators and multiple local search schemes, given an optimization problem at hand. Type 3: Algorithms that autonomously learn with experience, adaptively reusing data and/or machine learning models drawn from related problems as prior knowledge in new target tasks of interest; examples include, but are not limited to, transfer learning and optimization, multi-task learning and optimization, or any other multi-X evolutionary learning and optimization methodologies.