Our first works in target discovery using deep neural networks date back to 2015-2016 where we collaborated extensively with pharmaceutical and biotechnology companies as well as academic institutions to invent and test new ways to understand human biology. We built the first deep neural network-based system to predict human age using tissue-specific transcriptomic, proteomic, and other data types. Our first published work in target discovery with basic experimental validation was with a company called BioTime (now AgeX Therapeutics) called "Use of deep neural network ensembles to identify embryonic-fetal transition markers: repression of COX7A1 in embryonic and cancer cells"
since then, we built over one hundred different models that use different approaches to target discovery, incorporating best practices and centuries of human knowledge and experience.
Here we share our latest study results where we applied our end-to-end AI platform to tackle Idiopathic Pulmonary Fibrosis (IPF). IPF is a broad medical condition that is limited to the lungs and primarily affects older adults. As the disease progresses, the health of the patient gradually deteriorates leading to a potentially life-threatening condition. Fibrosis is one of the main aging-associated disease processes, and we can use deep neural networks trained on age and different types of fibrosis to identify a range of targets.
To build our initial hypothesis, we trained deep neural networks on a collection of omics and clinical datasets to predict tissue-specific fibrosis, as well as age and sex of the patients. We then applied a range of target discovery tools implemented in our PandaOmics
target discovery system and performed sophisticated gene and pathway scoring published in Nature Communications
, and came up with relevant targets via deep feature selection, causality inference, and de novo
pathway reconstruction. The target novelty and disease association scoring was assessed by a natural language processing (NLP) engine, which analyses data from millions of data files, including patents, research publications, grants, and databases of clinical trials. As a result, PandaOmics
revealed 20 targets for validation that we narrowed down to one novel intracellular target and prioritized for further analysis. Chemistry42
is our generative chemistry module for drug discovery. This module includes an ensemble of generative and scoring engines that "imagines" molecules from scratch using cutting-edge deep learning technologies we pioneered for medical use back in 2015. Chemistry42 automatically creates drug-like molecular structures with appropriate physicochemical properties. In this case, Chemistry42 was used to design a library of small molecules that bind to the novel intracellular target revealed by PandaOmics
The series of novel small molecules generated by Chemistry42
showed promising on target inhibition. One particular hit ISM001 demonstrated activity with nanomolar (nM) IC50 values. When optimizing ISM001, we managed to achieve increased solubility, good ADME properties, and no sign of CYP inhibition — with retained nanomolar potency. Interestingly, the optimized compounds also showed nanomolar potency against nine other targets related to fibrosis.