Over the last several years, the use of artificial intelligence (AI) in the pharma and biomedical industry has gone from science fiction to science fact. AI-driven automated processes and efficient predictive analytics tools have been increasingly driving unprecedented improvements and better outcomes across the entire pharmaceutical value chain.
“However, on the hind side, AI applications are not enough to improve the low productivity of drug R&D,” says Minsoo Choi, Ph.D. in Systems Biology,and Co-Founder and CEO of NetTargets. He cites his reasons for such a point of view. He explains, “Simply adding AI applications to a fragmented system will not create innovative changes. “Just AI” is not strong enough to identify hidden causal relationships or truly detailed mechanisms of complex biological processes. It cannot clarify the underlying mechanism and potential failure risks that are not superficial, and often not published in R&D studies.”
But, overcoming the limitation of “Just AI,” which is also referred to as the “Black Box Model,” is not simple, and the technology for it is very rare. “We need a breakthrough to unveil the black curtain,” emphasizes Choi. And this is what his company is perfectly poised to achieve. Established by KAIST professor Kwang-Hyun Cho, one of the pioneers in the field of Systems Biology, together with Choi and two other PhDs, NetTargets leverages ‘AI-enhanced systems biology’ to unravel the underlying hidden relationship, and enables a “White Box Model”
But, How is NetTargets Making This Possible?
NetTargets views AI from a different eye. It adopts AI to enhance its base expertise in systems biology. NetTargets has developed a computational modeling methodology that mimics the complex biological processes to understand and analyze the relationships in the biomolecular regulatory networks of a human cell. It achieves the methodology by building mathematical models that facilitate network dynamics analysis to clarify hidden feedback or crosstalks in multiple signaling pathways in biological networks. The company has integrated its expertise and developed its own database and platforms based on AI-enhanced systems biology. The database, so called N-DB, contains broad and specialized biomedical/chemical information. The first platform named N-MAP (NetTargets Mechanism Analysis Platform) identifies new novel target (set)s by constructing detailed and accurate biomolecular regulatory network, which enables the identification of novel key targets for therapeutic strategy; both single- and multi-targets, to fix the mutated network.
NetTargets has developed a computational modeling methodology that mimics the complex biological processes to understand and control dynamic behavior in the biomolecular regulatory networks
The second platform, N-CAP (NetTargets Compound Analysis Platform), predicts physico-chemical properties and produces promising compound candidates for the control of the resulted targets from N-MAP. “We have been able to accumulate many in-vivo-level-validated targets out from this technology, and some of the selected targets are on the development phase with N-CAP,” informs Choi.
Driving Tangible Success in the Pharma World
It goes without saying that today many companies are pouring in tremendous resources to develop new drugs. However, it is often seen that their success rate is very low. One of the main reasons for this phenomenon is that the development of such new drugs takes place without a sufficient understanding of the underlying mechanism for the disease that has innate complexity and genetic variation. Consequently, the drug candidates that only cure partial disorders with high risk of resistance go to clinical trials and result in a high probability of failing. NetTarget’s technology can remarkably increase the likelihood of success because it enables understanding the mechanism through network dynamics analysis and mechanism analysis based on the clarified causal relationships between nodes (representing biological molecules) in the biomolecular regulatory network. NetTargets constructs the biomolecular regulatory networks to reveal hidden mechanisms controlling biological phenomena by performing dynamic simulation of a mathematical model of information flow through the network. This approach, thus, creates more feasible/promising targets, contributes to the development of more promising drug candidates,and consecutively raises the success rate of new drugs. “Our mission is finding novel drug targets and lead first-in-class drug development by using our cutting-edge technology,i.e., ‘AI-enhanced systems biology,’” explains Choi.
Moreover, NetTargets’ platforms can estimate the common setbacks of low efficacy, toxicity, side effect, and drug resistance in advance by validating targets. Simply put, it means researchers can dodge the predicted risks and reach the desired drugs with lesser hassles in the middle. Furthermore, the pre-validation also shortens the experimental or clinical proof procedures significantly, leading to a more reliable drug. NetTargets has generated many pre-validated targets that are experimentally validated up to in-vivo levels. Those are now in the process of compound development. Those resulted targets in the list have much higher chances to get into the market. “Based on the bedrock of AI-enhanced systems biology, we have abstracted tailored targets, for example, a target for solid cancer that can cease the proliferation of cancer cells while not affecting the normal cells,” says Choi, proudly.
The value of these new kinds of drugs would significantly turn the direction of treatment toward a completely innovative path, and NetTargets would be at the helm of these paradigm shifts.