Bridging the AI Divide in Drug Discovery: Practical Lessons from Capacity Strengthening in Africa
TL;DR
The lessons learnt from a series of in-person and online workshops to grow the AI skills base for medicinal chemistry and enable adoption across regional research hubs are discussed, the fit-for-purpose open-source tools developed, and key considerations for future initiatives.
Abstract
Artificial Intelligence (AI) has become an integral part of drug discovery in high-income countries. However, these advances have yet to be broadly adopted by research ecosystems in many low- and middle-income countries in Africa, where understudied infectious diseases, compounded by a growing noncommunicable disease burden, cause millions of deaths each year. Given adequate support and appropriate models AI could play a transformative role in under-resourced biomedical research ecosystems in Africa, democratizing drug discovery research. There is an urgent need to invest in building the critical infrastructure, tools, and capacity required. To drive this agenda forward, Ersilia, the H3D Centre, and H3D Foundation with support from the AI2050 program at Schmidt Sciences hosted a series of in-person and online workshops in Africa to grow the AI skills base for medicinal chemistry and enable adoption across regional research hubs. In this perspective article, we discuss the lessons learnt from this program, the fit-for-purpose open-source tools developed, and key considerations for future initiatives.