GeneDisco Challenge for machine learning-enabled Drug discovery on 29 April
– Drug discovery has become an increasingly challenging endeavour: the success rate of developing new therapeutics has been historically low, but this rate has been steadily declining, says Stefan Bauer in an interview for KTH Researchers’ Noticeboard.
The average cost of bringing a new drug to market is twice as high as just a decade earlier. Machine learning-based approaches present a unique opportunity to address this challenge.
Machine learning methods, such as active and reinforcement learning, could aid in optimally exploring the vast biological area by integrating prior knowledge from various information sources. However, there exist no standardised benchmarks and data sets for this challenging task. To solve this problem, we created GeneDisco, a benchmark suite for evaluating active learning algorithms for experimental design in drug discovery, according to Stefan Bauer.
We aim to bring together the community to discuss cutting-edge research in machine learning-enabled drug discovery for the workshop.GeneDisco challenge will hopefully lead to a diverse set of proposed approaches, one of which might outperform the other methods consistently. If implemented at one of the big pharmaceutical companies, an increased success rate of 1 per cent can lead to tens or hundreds of new drugs every year, says Stefan Bauer.
For more information contact Stefan Bauer by mail: email@example.com
Interview with Stefan Bauer on Researchers’ Noticeboard