Prototype-Based Compound Discovery Using Deep Generative Models

Abstract

Designing a new drug is a lengthy and expensive process. As the space of potential molecules is very large ( Polishchuk , P. G. ; Madzhidov , T. I. ; Varnek , A. Estimation of the size of drug-like chemical space based on GDB-17 data . J. Comput.-Aided Mol. Des. 2013 , 27 , 675 -679 10.1007/s10822-013-9672-4 ), a common technique during drug discovery is to start from a molecule which already has some of the desired properties. An interdisciplinary team of scientists generates hypothesis about the required changes to the prototype. In this work, we develop a deep-learning unsupervised-approach that automatically generates potential drug molecules given a prototype drug. We show that the molecules generated by the system are valid molecules and significantly different from the prototype drug. Out of the compounds generated by the system, we identified 35 known FDA-approved drugs. As an example, our system generated isoniazid, one of the main drugs for tuberculosis. We suggest several ranking functions for the generated molecules and present results that the top ten generated molecules per prototype drug contained in our retrospective experiments 23 known FDA-approved drugs.

Constrained Bayesian optimization for automatic chemical design using variational autoencoders

“…The literature concerning generative models of molecules has exploded since the first work on the topic Gómez-Bombarelli et al [2018]. Current methods feature molecular representations such as SMILES [Janz et al, 2018, Segler et al, 2017, Skalic et al, 2019, Ertl et al, 2017, Lim et al, 2018, Kang and Cho, 2018, Sattarov et al, 2019, Gupta et al, 2018, Harel and Radinsky, 2018, Yoshikawa et al, 2018, Bjerrum and Sattarov, 2018, Mohammadi et al, 2019 and graphs [Simonovsky and Komodakis, 2018, Li et al, 2018a, De Cao and Kipf, 2018, Kusner et al, 2017, Dai et al, 2018, Samanta et al, 2019, Li et al, 2018b, Kajino, 2019, Jin et al, 2019, Bresson and Laurent, 2019, Lim et al, 2019, Pölsterl and Wachinger, 2019, Krenn et al, 2019, Maziarka et al, 2019, Madhawa et al, 2019, Shen, 2018, Korovina et al, 2019 In this section we conduct an empirical test of the hypothesis from [Gómez-Bombarelli et al, 2018] that the decoder's lack of efficiency is due to data point collection in "dead regions" of the latent space far from the data on which the VAE was trained. We use this information to construct a binary classification Bayesian Neural Network (BNN) to serve as a constraint function that outputs the probability of a latent point being valid, the details of which will be discussed in the section on labelling criteria.…”

Capsule Networks Showed Excellent Performance in the Classification of hERG Blockers/Nonblockers

“…They have widely been applied to fields particularly computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, and various games (Collobert and Weston, 2008;Bengio, 2009;Dahl et al, 2012;Hinton et al, 2012;LeCun et al, 2015;Defferrard et al, 2016;Mamoshina et al, 2016), where they have produced results comparable to or in some cases superior to human experts. In recent years, deep learning has also been applied to drug discovery, and it has demonstrated its potentials (Lusci et al, 2013;Ma et al, 2015;Xu et al, 2015;Aliper et al, 2016;Mayr et al, 2016;Pereira et al, 2016;Subramanian et al, 2016;Kadurin et al, 2017;Ragoza et al, 2017;Ramsundar et al, 2017;Xu et al, 2017;Ghasemi et al, 2018;Harel and Radinsky, 2018;Hu et al, 2018;Popova et al, 2018;Preuer et al, 2018;Russo et al, 2018;Segler et al, 2018;Shin et al, 2018;Cai et al, 2019;Wang et al, 2019a;Yang et al, 2019). However, there are still some issues that limit the application of deep learning in drug discovery.…”

In silico Strategies to Support Fragment-to-Lead Optimization in Drug Discovery

“…After this, a decoder was used to retrieve the molecules on the latent space into SMILES (Gómez-Bombarelli et al, 2018). Handling these DL methods in a multidimensional way, fragment hits can be optimized automatically taking into consideration several parameters such as bioactivity, solubility, synthetic feasibility, and ADMET properties, generating new compounds with optimized values for these parameters (Figure 7) (Olivecrona et al, 2017;Ramsundar et al, 2017;Gómez-Bombarelli et al, 2018;Harel and Radinsky, 2018;Li et al, 2018;Merk et al, 2018;Polykovskiy et al, 2018;Popova et al, 2018;Putin et al, 2018;Awale et al, 2019;Vamathevan et al, 2019).…”