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Found 5 papers, 3 papers with code
Accelerating Material Design with the Generative Toolkit for Scientific Discovery
With the growing availability of data within various scientific domains, generative models hold enormous potential to accelerate scientific discovery.
Pre-training Protein Language Models with Label-Agnostic Binding Pairs Enhances Performance in Downstream Tasks
Less than 1% of protein sequences are structurally and functionally annotated.
PaccMann$^{RL}$ on SARS-CoV-2: Designing antiviral candidates with conditional generative models
With the fast development of COVID-19 into a global pandemic, scientists around the globe are desperately searching for effective antiviral therapeutic agents.
PaccMann$^{RL}$: Designing anticancer drugs from transcriptomic data via reinforcement learning
The generative process is optimized through PaccMann, a previously developed drug sensitivity prediction model to obtain effective anticancer compounds for the given context (i. e., transcriptomic profile).
PIMKL: Pathway Induced Multiple Kernel Learning
Reliable identification of molecular biomarkers is essential for accurate patient stratification.
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