About the conference 

Over the past 100 years, the process of drug discovery by the pharmaceutical industry has dramatically changed. Initially, drug discovery was based on ethnopharmacological knowledge accompanied by target and mechanism identifications. The advent of modern molecular biology techniques, combined with our deepening understanding of the human genome, has profoundly transformed drug discovery. This evolution has decisively moved the field toward a hypothesis-driven, target-based approach, paving the way for more precise and effective treatments.

Modern drug discovery is a complex field that encompasses various scientific disciplines. It involves identifying new therapeutic candidates using computational and synthetic techniques, in vitro and in vivo evaluations, and translational models. Despite the significant progress, it remains a highly risky and costly endeavour that can take many years to bring a new molecule from concept to market. Thus, there is a need for an integrative system to speed up the drug discovery process, from the identification of compounds to the launching of the product.

Natural products and pharmaceutical chemistry are significant contributors to modern drug discovery. Natural products demonstrate remarkable structural diversity, and the secondary metabolites from plants, animals, and microbes are used to treat several human diseases.

Meanwhile, pharmaceutical chemistry is a rapidly advancing interdisciplinary field that integrates chemistry and pharmacy and uses target-based approaches to develop targeted medicines to treat specific diseases effectively. Sophisticated instruments, hyphenated-chromatographic techniques, and computer-assisted drug design (CADD) have opened new doors for finding newer leads. CADD plays a pivotal role in the initial stages of drug discovery, offering valuable tools for virtual ligand screening and in silico structure prediction, refinement, and optimisation. New technologies such as automation, machine learning, and artificial intelligence have been included in recent years to support and fasten the drug discovery program. Significant advancements have been made in structure prediction, data integration, and data analytics, all contributing to the acceleration of drug discovery processes. AI models can be used for the virtual screening of natural databases, identifying potential drug candidates, and assessing the pharmacological properties. Additionally, AI tools can be helpful in predicting the toxicity of compounds