Applications of Deep Learning in Molecule Generation and Molecular Property Prediction

财产(哲学) 分子 计算机科学 化学 纳米技术 材料科学 有机化学 哲学 认识论
作者
W. Patrick Walters,Regina Barzilay
出处
期刊:Accounts of Chemical Research [American Chemical Society]
卷期号:54 (2): 263-270 被引量:264
标识
DOI:10.1021/acs.accounts.0c00699
摘要

ConspectusRecent advances in computer hardware and software have led to a revolution in deep neural networks that has impacted fields ranging from language translation to computer vision. Deep learning has also impacted a number of areas in drug discovery, including the analysis of cellular images and the design of novel routes for the synthesis of organic molecules. While work in these areas has been impactful, a complete review of the applications of deep learning in drug discovery would be beyond the scope of a single Account. In this Account, we will focus on two key areas where deep learning has impacted molecular design: the prediction of molecular properties and the de novo generation of suggestions for new molecules.One of the most significant advances in the development of quantitative structure–activity relationships (QSARs) has come from the application of deep learning methods to the prediction of the biological activity and physical properties of molecules in drug discovery programs. Rather than employing the expert-derived chemical features typically used to build predictive models, researchers are now using deep learning to develop novel molecular representations. These representations, coupled with the ability of deep neural networks to uncover complex, nonlinear relationships, have led to state-of-the-art performance. While deep learning has changed the way that many researchers approach QSARs, it is not a panacea. As with any other machine learning task, the design of predictive models is dependent on the quality, quantity, and relevance of available data. Seemingly fundamental issues, such as optimal methods for creating a training set, are still open questions for the field. Another critical area that is still the subject of multiple research efforts is the development of methods for assessing the confidence in a model.Deep learning has also contributed to a renaissance in the application of de novo molecule generation. Rather than relying on manually defined heuristics, deep learning methods learn to generate new molecules based on sets of existing molecules. Techniques that were originally developed for areas such as image generation and language translation have been adapted to the generation of molecules. These deep learning methods have been coupled with the predictive models described above and are being used to generate new molecules with specific predicted biological activity profiles. While these generative algorithms appear promising, there have been only a few reports on the synthesis and testing of molecules based on designs proposed by generative models. The evaluation of the diversity, quality, and ultimate value of molecules produced by generative models is still an open question. While the field has produced a number of benchmarks, it has yet to agree on how one should ultimately assess molecules "invented" by an algorithm.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
慕青应助福缘采纳,获得10
刚刚
1秒前
4秒前
xt_489完成签到,获得积分10
5秒前
JamesPei应助聪明的白筠采纳,获得10
6秒前
今后应助水中鱼采纳,获得10
6秒前
书虫发布了新的文献求助10
9秒前
10秒前
郭阳发布了新的文献求助10
10秒前
10秒前
13秒前
14秒前
醉熏的鑫完成签到,获得积分10
14秒前
李蕤蕤完成签到,获得积分10
15秒前
小唐尼发布了新的文献求助30
15秒前
16秒前
17秒前
Huang_being发布了新的文献求助10
18秒前
shawn发布了新的文献求助10
18秒前
18秒前
FashionBoy应助dara采纳,获得10
19秒前
着急的大米完成签到,获得积分20
20秒前
orixero应助hello采纳,获得10
20秒前
星辰大海应助顺利煎蛋采纳,获得10
21秒前
21秒前
23秒前
量子星尘发布了新的文献求助10
24秒前
24秒前
GGBOND发布了新的文献求助10
24秒前
25秒前
wu发布了新的文献求助10
25秒前
纳米酶催化完成签到,获得积分10
26秒前
pppp发布了新的文献求助10
27秒前
桐桐应助林宝雯采纳,获得10
27秒前
程程发布了新的文献求助10
29秒前
完美世界应助着急的大米采纳,获得10
29秒前
29秒前
29秒前
bkagyin应助冰琪采纳,获得10
29秒前
万能图书馆应助anna采纳,获得10
31秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3989115
求助须知:如何正确求助?哪些是违规求助? 3531367
关于积分的说明 11253688
捐赠科研通 3269986
什么是DOI,文献DOI怎么找? 1804868
邀请新用户注册赠送积分活动 882078
科研通“疑难数据库(出版商)”最低求助积分说明 809105