24/7 BIOPHARMA - issue 1 / October 2024

LOGICA

and interactions within the human body. This

advancement holds the key to personalised medicine, where treatments can be tailored to the genetic makeup of individual patients [ 6] . AI’s ability to sift through genetic information and correlate it with drug responses will open new avenues for customised therapies, making treatments more effective and reducing the incidence of adverse reactions.

Exploring complex disease pathways

As AI technologies mature, their application will extend beyond traditional drug

design to explore complex biological systems and disease pathways. AI models will be instrumental in uncovering novel targets and understanding the multi-faceted nature of diseases like oncology, CNS, cardiovascular and autoimmune disorders, to name a few. By simulating the interactions within these systems, AI will identify new therapeutic opportunities and guide the development of drugs that can modulate disease processes more effectively. The future will also see a deeper integration of AI with experimental validation, creating a seamless feedback loop that accelerates the discovery process. This synergy will ensure that AI-designed molecules are not only theoretically effective, but also validated through empirical data. The continuous exchange of data between AI models and laboratory results will refine AI predictions, making the drug discovery process more efficient and reliable. In addition, this integration will facilitate the development of more sophisticated AI algorithms, thereby enhancing the predictive accuracy for drug efficacy and safety profiles before clinical trials commence. Furthermore, the democratisation of drug discovery through AI enables all levels of research to leverage cutting-edge research tools, broadening the scope of innovation and potentially reducing the time and cost to market for new treatments. AI and lab data synergy

underscores the importance of collaboration and data sharing. Openly sharing data, critical analyses and methods allows AI to aid in various aspects of drug discovery, including in silico property prediction and the identification of effective drug candidates. AI algorithms can efficiently scan large compound libraries for potential candidates, thus showcasing their ability to guide experimental screening efforts with limited initial data. The future of drug discovery is being reshaped by the convergence of AI and experimental data, a partnership that promises to revolutionise how we design molecules and approach therapeutic challenges [ 5] . As we look ahead, AI’s role in drug discovery is poised to transition from an auxiliary tool to a central figure in the design, testing and optimisation of new compounds. This shift is predicated on AI’s ability to process vast datasets, drawing insights that would otherwise be unattainable to human researchers alone. The integration of AI with cutting-edge experimental techniques will enable the design of molecules with unprecedented specificity and efficacy, reducing the time and cost associated with bringing new drugs to market. The future of AI-designed drugs

Toward personalised medicine

The evolution of AI models, fuelled by richer and more comprehensive datasets, will lead to more accurate predictions of drug behaviour

36 TWENTYFOURSEVENBIOPHARMA Issue 1 / October 2024