Evolving molecules using multi-objective optimization: applying to ADME/Tox

被引：63

作者：

Ekins, Sean ^{[1
,2
,3
,4
]}

Honeycutt, J. Dana ^{[5
]}

Metz, James T. ^{[6
]}

机构：

[1] Collaborat Chem, Jenkintown, PA 19046 USA

[2] Univ Maryland, Dept Pharmaceut Sci, Baltimore, MD 21201 USA

[3] Univ Med & Dent New Jersey, Robert Wood Johnson Med Sch, Dept Pharmacol, Piscataway, NJ 08854 USA

[4] Collaborat Drug Discovery, Burlingame, CA 94010 USA

[5] Accelrys, San Diego, CA 92121 USA

[6] Abbott Labs, Dept Fragment Screening & Lead Characterizat, GPRD R4DG, Abbott Pk, IL 60064 USA

来源：

DRUG DISCOVERY TODAY | 2010年 / 15卷 / 11-12期

关键词：

COMBINATORIAL LIBRARY DESIGN; BRAIN-BARRIER PERMEABILITY; DRUG-LIKE MOLECULES; IN-SILICO; EVOLUTIONARY OPTIMIZATION; GENETIC ALGORITHM; LIGAND EFFICIENCY; PHARMACOPHORE IDENTIFICATION; SELECTIVE OPTIMIZATION; COMPOUND OPTIMIZATION;

D O I：

10.1016/j.drudis.2010.04.003

中图分类号：

R9 [药学];

学科分类号：

1007 ;

摘要：

Modern drug discovery involves the simultaneous optimization of many physicochemical and biological properties that transcends the historical focus on bioactivity alone. The process of resolving many requirements is termed 'multi-objective optimization', and here we discuss how this can be used for drug discovery, focusing on evolutionary molecule design to incorporate optimal predicted absorption, distribution, metabolism, excretion and toxicity properties. We provide several examples of how Pareto optimization implemented in Pareto Ligand Designer can be used to make trade-offs between these different predicted or real molecular properties to result in better predicted compounds.

引用

页码：451 / 460

页数：10

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