Solution-Phase DNA-Compatible Pictet-Spengler Reaction Aided by Machine Learning Building Block Filtering.
Li Ke,Xiaohong Liu,Sixiu Liu,Yulong An,Yanfang Shen,Qingxia Sun,Xiaodong Shi,Wenji Su,Weiren Cui,Zhiqiang Duan,Kuai Letian,Yang Hongfang,Alexander L. Satz,Kaixian Chen,Kaixian Chen,Hualiang Jiang,Hualiang Jiang,Mingyue Zheng,Peng Xuanjia,Xiaojie Lu +19 more
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TLDR
A machine learning algorithm has been developed that predicts the conversion rate for the DNA-compatible reaction of a building block with a model DNA-conjugate, allowing for a challenging reaction, with an otherwise very low building block pass rate in the test reaction, to still be used in DEL synthesis.About:
This article is published in iScience.The article was published on 2020-06-26 and is currently open access. It has received 15 citations till now.read more
Citations
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Pattern Recognition and Machine Learning
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Journal ArticleDOI
DNA-Encoded Chemical Libraries: A Comprehensive Review with Succesful Stories and Future Challenges.
TL;DR: An overview of diverse approaches for the generation and screening of DEL molecular repertoires is presented, detailing how novel ligands were isolated from DEL screening campaigns and were further optimized by medicinal chemistry.
Journal ArticleDOI
Encoded Library Technologies as Integrated Lead Finding Platforms for Drug Discovery.
TL;DR: This review discusses how encoded peptide libraries and DNA-encoded libraries are applied in research, and why Novartis considers it beneficial to run both pipelines in-house.
Journal ArticleDOI
DNA-encoded libraries (DELs): a review of on-DNA chemistries and their output
TL;DR: A series of novel DNA-compatible chemistry reactions for DEL building blocks are summarized and the druggability of screened hit molecules via DELs in the past five years is analysed.
References
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Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
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Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
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Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
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Dropout: a simple way to prevent neural networks from overfitting
TL;DR: It is shown that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets.
Posted Content
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: In this article, the adaptive estimates of lower-order moments are used for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimate of lowerorder moments.