The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology

Abstract

Recent advances in deep learning and specifically in generative adversarial networks have demonstrated surprising results in generating new images and videos upon request even using natural language as input. In this paper we present the first application of generative adversarial autoencoders (AAE) for generating novel molecular fingerprints with a defined set of parameters. We developed a 7-layer AAE architecture with the latent middle layer serving as a discriminator. As an input and output the AAE uses a vector of binary fingerprints and concentration of the molecule. In the latent layer we also introduced a neuron responsible for growth inhibition percentage, which when negative indicates the reduction in the number of tumor cells after the treatment. To train the AAE we used the NCI-60 cell line assay data for 6252 compounds profiled on MCF-7 cell line. The output of the AAE was used to screen 72 million compounds in PubChem and select candidate molecules with potential anti-cancer properties. This approach is a proof of concept of an artificially-intelligent drug discovery engine, where AAEs are used to generate new molecular fingerprints with the desired molecular properties.

Extracting a biologically relevant latent space from cancer transcriptomes with variational autoencoders

“…10,11 For instance, a recent group trained adversarial autoencoders on chemical compound structures and their growth inhibiting effects in cancer cell lines to learn manifold spaces of effective small molecule drugs. 12,13 Additionally, Rampasek et al trained a VAE to learn a gene expression manifold of reactions of cancer cell lines to drug treatment perturbation. 14 The theoretical basis for modeling cancer using lower dimensional manifolds is established, as it has been previously hypothesized that cancer exists in "basins of attraction" defined by specific pathway aberrations that drive cells toward cancer states.…”

Human microbiome aging clocks based on deep learning and tandem of permutation feature importance and accumulated local effects

“…The renaissance of deep learning that started in 2015 resulted in unprecedented machine learning performance in image, voice, and text recognition, as well as a range of biomedical applications 29 such as drug repurposing 30 and target identification 31 . One of the most impactful applications of DL in biomedicine was in the applications of generative models to de novo molecular design [32][33][34][35][36] . In the context of aging research, these new methods can be combined for geroprotector discovery [37][38][39][40][41] .…”

Unsupervised deep learning with variational autoencoders applied to breast tumor genome-wide DNA methylation data with biologic feature extraction

“…This in turn provides an opportunity to study genome-wide interactions in ways that may be computationally intractable using traditional statistical modeling approaches. Indeed, to date, deep learning have been used to generate photo-realistic cell images [3], learn functional representations of neural in-situ hybridization images [4], to predict novel drug targets [5,6,7,8], and to model the hierarchical structure and function of the cell [9]. Recently, VAE methods have also been shown to learn biologically-relevant latent representations of tumors using genome-scale gene expression [10] and DNA methylation data11.…”