Chemical Space Mimicry for Drug Discovery

By William Yuan, D. Jiang, Dhanya K. Nambiar, L. Liew, Michael P. Hay, Joshua Bloomstein, Peter Lu, Brandon Turner, Quynh-Thu Le, Robert Tibshirani and 3 more. 2017

80 citations recorded by the source corpus.

We describe a new library generation method, Machine-based Identification of Molecules Inside Characterized Space (MIMICS), that generates sets of molecules inspired by a text-based input. MIMICS-generated libraries were found to preserve distributions of properties while simultaneously increasing structural diversity. Newly identified MIMICS-generated compounds were found to be bioactive as inhibitors of specific components of the unfolded protein response (UPR) and the VEGFR2 pathway in cell-based assays, thus confirming the applicability of this methodology toward drug design applications. Wider application of MIMICS could facilitate the efficient utilization of chemical space.

Subfields: Computational Theory and Mathematics, Molecular Biology, Pharmacology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

In Molecular Biology

In Pharmacology

Citations in the corpus

GuacaMol: Benchmarking Models for De Novo Molecular Design (2018, methodology): “Generative models based on neural networks have since been further explored, with work on VAEs, 17,36–45 RNNs, 46–58 generative adversarial networks (GANs), 54,55,59,60 adversarial au-toencoders (AAEs), 61–64 and graph convolutional networks, 16,57,60,65 using SMILES strings or graphs to represent m”
A Structure-Based Drug Discovery Paradigm (2019, background): “[126] have used a recurrent neural network (RNN) for new structure generation acknowledging its success in natural language processing.”
Rethinking drug design in the artificial intelligence era (2019, background): “…of supervised feedforward networks 166 (part a ), supervised and semi-supervised variational autoencoders (part b ), recurrent neural networks 167,168 (part c ) and generative adversarial networks (part d ). models based on reinforcement methods often play a large role in computational…”
Molecular generative model based on conditional variational autoencoder for de novo molecular design (2018, background): “[16] designed potential inhibitors for a target protein and tested them in experiments.”
Artificial Intelligence For Natural Product Drug Discovery (2023, background): “…109–112 ; for instance, for predicting drug–target binding affinity 113 , SMILES-based approaches for de novo drug-like molecule generation 114,115 , graph-based de novo molecular generation 116 , and property prediction 117,118 and surface mesh-based approaches for protein…”
Application of Generative Autoencoder in De Novo Molecular Design (2017, background): “Segler et al. [2] and Yuan et al. [3] applied a recurrent neural network (RNN) to a large number of chemical structures represented as SMILES strings to obtain generative models which learn the probability distribution of characters in a SMILES string.”
The rise of deep learning in drug discovery. (2018, methodology): “Segler et al. [64] and Yuan et al. [65] reported their studies using RNNs to generate novel chemical structures.”