AI-enabled cancer target prioritization with optimal profiles balancing novelty, confidence and commercial tractability

Author:

Long Xi1ORCID,Steurer Barbara1ORCID,Wong Chun Wai1,Kozlova Ekaterina1ORCID,Naumov Vladimir1ORCID,Pun Frank W1ORCID,Aliper Alex2ORCID,Ren Feng3ORCID,Zhavoronkov Alex124ORCID

Affiliation:

1. Insilico Medicine Hong Kong Ltd., Hong Kong Science and Technology Park, Hong Kong, Hong Kong SAR, China

2. Insilico Medicine AI Limited, Level 6, Unit 08, Block A, IRENA HQ Building, Masdar City, Abu Dhabi, United Arab Emirates

3. Insilico Medicine Shanghai Ltd., Suite 902, Tower C, Changtai Plaza, 2889 Jinke Road, Pudong, Shanghai, 201203, China

4. Buck Institute for Research on Aging, Novato, CA, USA

Abstract

The identification of new biological targets is crucial to advance cancer therapy, but deciphering the fiendishly complex processes that drive and sustain disease can be tedious and resource intensive. To optimize and accelerate the drug discovery process, artificial intelligence (AI) platforms are emerging that enable fast and cost-effective identification and prioritization of novel and disease-specific therapeutic targets with optimal target profiles, balancing confidence, novelty and commercial tractability. AI-streamlined target profiling has the potential to significantly improve the commercial burden of traditional drug development, and provides an unbiased approach for novel target identification. Here, we discuss the AI-assessed target profile and clinical relevance of genes recently identified by our AI-driven target discovery platform as top priority cancer targets.

Publisher

Informa UK Limited

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