化学信息学
计算机科学
药物发现
代表(政治)
化学生物学
重新调整用途
生物学数据
数据科学
领域(数学)
简单(哲学)
计算生物学
生化工程
人工智能
纳米技术
化学
生物
生物信息学
数学
认识论
工程类
生态学
哲学
纯数学
法学
材料科学
政治
生物化学
政治学
作者
Adrià Fernández‐Torras,Arnau Comajuncosa-Creus,Miquel Duran‐Frigola,Patrick Aloy
标识
DOI:10.1016/j.cbpa.2021.09.001
摘要
Through the representation of small molecule structures as numerical descriptors and the exploitation of the similarity principle, chemoinformatics has made paramount contributions to drug discovery, from unveiling mechanisms of action and repurposing approved drugs to de novo crafting of molecules with desired properties and tailored targets. Yet, the inherent complexity of biological systems has fostered the implementation of large-scale experimental screenings seeking a deeper understanding of the targeted proteins, the disrupted biological processes and the systemic responses of cells to chemical perturbations. After this wealth of data, a new generation of data-driven descriptors has arisen providing a rich portrait of small molecule characteristics that goes beyond chemical properties. Here, we give an overview of biologically relevant descriptors, covering chemical compounds, proteins and other biological entities, such as diseases and cell lines, while aligning them to the major contributions in the field from disciplines, such as natural language processing or computer vision. We now envision a new scenario for chemical and biological entities where they both are translated into a common numerical format. In this computational framework, complex connections between entities can be unveiled by means of simple arithmetic operations, such as distance measures, additions, and subtractions.
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