Molexar is a unified multimodal molecular foundation model built on Fragment-SELFIES that uses pretraining followed by supervised fine-tuning with in-place condition embedding to handle scalar properties, pharmacophores, proteins, and pockets in one autoregressive path.
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Boltz-2 and fine-tuned DrugFormDTA lead ML-based binding prediction while GNINA leads docking tools on a cleaned antiviral dataset, with performance varying by viral protein.
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Molexar: A Unified Multimodal Molecular Foundation Model for Drug Design
Molexar is a unified multimodal molecular foundation model built on Fragment-SELFIES that uses pretraining followed by supervised fine-tuning with in-place condition embedding to handle scalar properties, pharmacophores, proteins, and pockets in one autoregressive path.
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Benchmarking open-source tools for in silico antiviral drug discovery
Boltz-2 and fine-tuned DrugFormDTA lead ML-based binding prediction while GNINA leads docking tools on a cleaned antiviral dataset, with performance varying by viral protein.