Looking to narrow down the possibilities quickly, Lopez turned to Semion Saikin, co-founder and chief science officer at Kebotix. The Cambridge, Massachusetts, startup uses machine learning algorithms and artificial intelligence tools to speed the discovery of new molecules with a specified set of properties. The partners set their sights on molecules that could accumulate in a patient’s tumor without harming healthy tissue. The right candidates for this approach would, after absorbing red or near infra-red light, also need to activate nearby oxygen molecules to selectively destroy the malignant cells.
Next, the researchers turned to an open-access database of light-absorbing molecules and their properties published by Lopez. Drawing from it, they began training their algorithms to recognize the molecular patterns of potentially suitable photodynamic therapy drugs. A $750,000 grant from the Massachusetts Life Sciences Center supports this work, as well as a full-time data scientist with a rare blend of chemistry, physics, and machine learning and computational expertise.