Insilico Medicine rebrands Pandomics as PandaOmics, releases a new version

Thursday, November 12, 2020 — Insilico Medicine today announced the release of a new version of its flagship AI-powered biological target discovery system and a name change from Pandomics to PandaOmics. The original name intended to reflect that the system will handle all (pan-) omics data types and integrated the company mascot – Panda (in 2016 the company published its iPANDA algorithm for dimensionality reduction); however, due to the coronavirus pandemic, the Pandomics name got closely associated with pandemics, and the company is in the process of renaming the system.

The PandaOmics v1.02 has multiple bug fixes and several additional features requested by the customers:

1. Omics dataset search by therapeutic area name;

2. The ability to create meta-analysis for several Omics datasets.

3. Target ID – a collection of AI based scores that proposes actionable targets based on molecular data (analysed in PandaOmics) and previously published text-based data.

“A lot of effort has been put in the development of PandaOmics therapeutic target discovery platform. Despite the name change, we will preserve our high quality of standards and ensure that the platform will help even larger number of researchers with their drug discovery programs”, said Ivan Ozerov, Target Discovery Director at Insilico Medicine, responsible for PandaOmics development from its early onset.

“While the majority of the customers got used to and liked the name Pandomics, due to COVID-19 the popular search engines started autocorrecting the name to pandemics, and we decided to make this change. Panda remains our company’s mascot and we are making a minor change to the name – PandaOmics. The system is designed to provide the biomedical community with the ability to identify biological targets using gene and protein expression data as well as other data types, evaluating the novelty, assessing and annotating these targets, and performing virtual validation of these targets using prior knowledge”, said Alex Zhavoronkov, PhD, CEO of Insilico Medicine.

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Since 2014 Insilico Medicine is focusing on generative models, reinforcement learning (RL), and other modern machine learning techniques for the generation of new molecular structures with the specified parameters, generation of synthetic biological data, target identification, and prediction of clinical trials outcomes. Recently, Insilico Medicine secured $37 million in series B funding. Since its inception, Insilico Medicine raised over $52 million, published over 100 peer-reviewed papers, applied for over 25 patents, and received multiple industry awards. Website http://insilico.com/