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从亲和力到超越:药物设计与发现的个人反思。

To Affinity and Beyond: A Personal Reflection on the Design and Discovery of Drugs.

机构信息

Independent Interdisciplinary Consultant, Oxford, UK.

出版信息

Molecules. 2022 Nov 7;27(21):7624. doi: 10.3390/molecules27217624.

DOI:10.3390/molecules27217624
PMID:36364451
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9656231/
Abstract

Faced with new and as yet unmet medical need, the stark underperformance of the pharmaceutical discovery process is well described if not perfectly understood. Driven primarily by profit rather than societal need, the search for new pharmaceutical products-small molecule drugs, biologicals, and vaccines-is neither properly funded nor sufficiently systematic. Many innovative approaches remain significantly underused and severely underappreciated, while dominant methodologies are replete with problems and limitations. Design is a component of drug discovery that is much discussed but seldom realised. In and of itself, technical innovation alone is unlikely to fulfil all the possibilities of drug discovery if the necessary underlying infrastructure remains unaltered. A fundamental revision in attitudes, with greater reliance on design powered by computational approaches, as well as a move away from the commercial imperative, is thus essential to capitalise fully on the potential of pharmaceutical intervention in healthcare.

摘要

面对新的、尚未满足的医疗需求,如果说制药发现过程表现不佳还不能完全被理解的话,那么这一说法是恰当的。受利润而非社会需求驱动,新药(小分子药物、生物制品和疫苗)的研发既没有得到适当的资金支持,也没有足够的系统性。许多创新方法仍未得到充分利用和重视,而主流方法则存在诸多问题和局限性。设计是药物发现的一个组成部分,虽然讨论得很多,但很少付诸实践。如果必要的基础结构保持不变,仅靠技术创新本身不太可能充分实现药物发现的所有可能性。因此,必须从根本上改变态度,更加依赖由计算方法驱动的设计,同时摆脱商业压力,从而充分利用药物干预在医疗保健中的潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d317/9656231/33e9a34cc953/molecules-27-07624-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d317/9656231/8f20f2fa7a80/molecules-27-07624-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d317/9656231/33e9a34cc953/molecules-27-07624-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d317/9656231/8f20f2fa7a80/molecules-27-07624-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d317/9656231/33e9a34cc953/molecules-27-07624-g002.jpg

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1
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2
MegaSyn: Integrating Generative Molecular Design, Automated Analog Designer, and Synthetic Viability Prediction.MegaSyn:整合生成性分子设计、自动化类似物设计和合成可行性预测
ACS Omega. 2022 May 27;7(22):18699-18713. doi: 10.1021/acsomega.2c01404. eCollection 2022 Jun 7.
3
PD-1 Blockade in Mismatch Repair-Deficient, Locally Advanced Rectal Cancer.
PD-1 阻断在错配修复缺陷、局部晚期直肠癌中的应用。
N Engl J Med. 2022 Jun 23;386(25):2363-2376. doi: 10.1056/NEJMoa2201445. Epub 2022 Jun 5.
4
ColabFold: making protein folding accessible to all.ColabFold:让蛋白质折叠变得人人可用。
Nat Methods. 2022 Jun;19(6):679-682. doi: 10.1038/s41592-022-01488-1. Epub 2022 May 30.
5
Has DeepMind's AlphaFold solved the protein folding problem?深度思维公司的阿尔法折叠算法解决了蛋白质折叠问题吗?
Biotechniques. 2022 Mar;72(3):73-76. doi: 10.2144/btn-2022-0007. Epub 2022 Feb 4.
6
Mycobacterium abscessus drug discovery using machine learning.利用机器学习发现脓肿分枝杆菌药物。
Tuberculosis (Edinb). 2022 Jan;132:102168. doi: 10.1016/j.tube.2022.102168. Epub 2022 Jan 20.
7
G-quadruplex DNA: a novel target for drug design.G-四链体 DNA:药物设计的新靶标。
Cell Mol Life Sci. 2021 Oct;78(19-20):6557-6583. doi: 10.1007/s00018-021-03921-8. Epub 2021 Aug 30.
8
molecular drug design benchmarking.分子药物设计基准测试
RSC Med Chem. 2021 Jun 3;12(8):1273-1280. doi: 10.1039/d1md00074h. eCollection 2021 Aug 18.
9
Emulating Randomized Clinical Trials With Nonrandomized Real-World Evidence Studies: First Results From the RCT DUPLICATE Initiative.基于真实世界证据的非随机研究模拟随机对照试验:RCT DUPLICATE 计划的初步结果。
Circulation. 2021 Mar 9;143(10):1002-1013. doi: 10.1161/CIRCULATIONAHA.120.051718. Epub 2020 Dec 17.
10
Artificial intelligence in chemistry and drug design.化学与药物设计中的人工智能
J Comput Aided Mol Des. 2020 Jul;34(7):709-715. doi: 10.1007/s10822-020-00317-x.