2021
DOI: 10.2174/0929867328666210729115728
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Trends in Deep Learning for Property-driven Drug Design

Abstract: : It is more pressing than ever to reduce the time and costs for developing lead compounds in the pharmaceutical industry. The co-occurrence of advances in high-throughput screening and the rise of deep learning (DL) have enabled the development of large-scale multimodal predictive models for virtual drug screening. Recently, deep generative models have emerged as a powerful tool for exploring the chemical space and raising hopes to expedite the drug discovery process. Following this progress in chemocentric … Show more

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Cited by 15 publications
(11 citation statements)
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“…The potential profits of DL in rational drug discovery were recently reviewed in the references [ 40 , 41 , 42 , 43 ]. Multilayer structures enable the extraction of cascade features that work with nonlinear functions [ 2 ].…”
Section: Deep Learning For Processing Molecular Data In Drug Designmentioning
confidence: 99%
See 1 more Smart Citation
“…The potential profits of DL in rational drug discovery were recently reviewed in the references [ 40 , 41 , 42 , 43 ]. Multilayer structures enable the extraction of cascade features that work with nonlinear functions [ 2 ].…”
Section: Deep Learning For Processing Molecular Data In Drug Designmentioning
confidence: 99%
“…The DL lexicon uses the term generative models (generative chemistry) for unsupervised algorithms to stress the difference between the classical design based on the local domain molecular exploration vs. the DL systematic continuous screening. Born and Manica [ 42 ] predicted that multimodal deep learning chemistry using disparate sources to generate molecules would be the next challenge in DL in the near future. The Variational Autoencoder (VAE) method [ 44 ] was developed as an algorithm to learn continuous molecular representations.…”
Section: Deep Learning For Processing Molecular Data In Drug Designmentioning
confidence: 99%
“…Deep learning can be employed for the prediction of drug–target interactions (DTIs), de novo molecular design, synthesis prediction, etc. [ 14 , 15 , 16 ]. While several studies have reviewed the contribution of deep learning in drug development, in vivo evaluation of the published algorithms is limited.…”
Section: Introductionmentioning
confidence: 99%
“…54 In that work, deep learning methods discovered potential DDR1 inhibitors of which two were found active in vitro and one even in vivo. 54 The most common type of deep generative model for molecular design is variational autoencoders (VAEs), 55 which can be seen as a global search method in the chemical space. VAEs have been coupled with Bayesian optimization (BO) principles by means of Gaussian processes (GPs) to optimize chemocentric properties such as drug-likeness.…”
Section: ■ Introductionmentioning
confidence: 99%
“…In that work, deep learning methods discovered potential DDR1 inhibitors of which two were found active in vitro and one even in vivo . The most common type of deep generative model for molecular design is variational autoencoders (VAEs), which can be seen as a global search method in the chemical space. VAEs have been coupled with Bayesian optimization (BO) principles by means of Gaussian processes (GPs) to optimize chemocentric properties such as drug-likeness. , While GP optimization allows us to maximize computationally costly functions efficiently, no previous work has, according to the best of our knowledge, incorporated an additional modality (like proteins) into the evaluation function and thus optimized a more complex, biochemical property.…”
Section: Introductionmentioning
confidence: 99%