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Found 3 papers, 3 papers with code
Bio-xLSTM: Generative modeling, representation and in-context learning of biological and chemical sequences
While Transformers have yielded impressive results, their quadratic runtime dependency on the sequence length complicates their use for long genomic sequences and in-context learning on proteins and chemical sequences.
Context-enriched molecule representations improve few-shot drug discovery
Our novel concept for molecule representation enrichment is to associate molecules from both the support set and the query set with a large set of reference (context) molecules through a Modern Hopfield Network.
Large-scale ligand-based virtual screening for SARS-CoV-2 inhibitors using deep neural networks
Due to the current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, there is an urgent need for novel therapies and drugs.
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