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Target Identification

Target identification

Target identification is one of the most crucial steps in drug discovery. Almost half of the clinical trials failed due to in-efficacy of selected drugs. One of the reasons for this is inadequate target validation. Therefore, it is very important to understand and explore the role of all the potential molecules involved in the disease pathophysiology.

Innoplexus leverages its AI based discovery engine to identify the most promising target with high biological relevance and market potential. The advantage of AI-based process are: an automation of manual and labor intensive tasks, an ability to process, integrate and make sense of the large volumes of complex and unstructured datasets and an AI-enhanced process of knowledge discovery for understanding their cross-connections in biological network, which is especially important in the early phases of drug discovery

AI can accelerate the target identification process and it can optimize the identification and optimization of lead molecule discovery. AI does so by searching through the past knowledge of the compounds, by examining a large variety of combinations and by recommending the most suitable leads. In particular, deep learning methods can explore existing data to help predict how tissue or body systems may respond to a given drug. Further, AI can play a role in the identification of target patient populations for clinical trials, in predicting disease progression based on molecular data obtained from tissue samples and in stratifying patients either in the clinical trials preparation phase or in the optimization of the treatments based on patients’ responses and their
individual characteristics.