Urology


Amplifying intelligent drug design

#artificialintelligence

'The idea of understanding a disease from an evolutionary viewpoint to inform drug design still resonates today in how Exscientia is approaching the design of anticancer agents. 'I spent a season at the GlaxoWellcome labs in Stevenage making the compounds I'd designed, and vividly remember the excitement of discovering the first molecule we'd made was active.' These included topics such as the druggable genome, ligand efficiency and network pharmacology – all of which are familiar topics to drug discovery chemists today. An early success involved feeding historical data for the project that discovered erectile dysfunction drug tadalafil (Cialis) into the evolutionary drug design model.


From America to Viagra: the art of finding what you're not looking for

The Japan Times

Among the chance discoveries that have been honored with the prestigious prize are X-rays (physics, 1901), penicillin (medicine, 1945), fullerenes that paved the way for nanotechnology (chemistry, 1996), conductive polymers (chemistry, 2000), and the bacteria responsible for ulcers (medicine, 2005). He was rewarded with the first Nobel physics prize awarded in 1901. Positive serendipity (Roentgen finds something he is not looking for, and confirms it through further study). Negative serendipity (Columbus finds something he is not looking for .


BioData Mining

#artificialintelligence

Most biological network inference methods focus on the definition of gene regulatory networks, in which edges represent direct regulatory interactions between genes [2–4]. Two approaches to functional network inference: one based on the expression profile similarity and the other based on the extraction of knowledge from machine learning models. The specific focus of this paper is the network inference from rule-based machine learning models, these have been successfully applied before to extract knowledge from genetic data [17] and identify disease risk factors in a bladder cancer study [18]. To address these questions, we propose in this article a new network inference protocol, called FuNeL (Functional Network Learning).