As reported in EurekAlert!, Insilico Medicine an artificial intelligence company developing an end-to-end drug discovery pipeline for age-related diseases, announced an open research collaboration. Researchers are invited to contribute to the new platform MOSES (Molecular Sets), described in the paper titled “Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models”. The code and the paper are available at the GitHub repository.

The original benchmarking platform is a result of collaboration between Insilico MedicineNeuromation, and Alán Aspuru-Guzik’s laboratory. The researchers and teams are invited to contribute their datasets and models to extend the benchmarking platform.

The paper introduces Molecular Sets (MOSES)—a benchmarking platform that encompasses various machine learning techniques, in order to compare them on a standard dataset. MOSES implements several popular molecular generation models and ranks them, according to a predefined set of metrics. MOSES aims to increase the pace of drug discovery and facilitate sharing and comparison of new models. MOSES is supposed to boost AI-powered drug discovery, just as ImageNet boosted deep learning for imaging data.

The ongoing research in machine learning, in particular, deep learning, brings up the issues of reproducibility and fair comparison of different approaches. While there are multiple methods for generating novel molecular structures with machine learning models, there is no conventional way to run and evaluate the performance of these generative models. The MOSES platform provides a standardized benchmarking dataset, a set of open-sourced models with unified implementation, and metrics to evaluate and assess the results of generation.

Source: EurekAlert!

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