Transformer Decoder Learns from a Pretrained Protein Language Model to Generate Ligands with High Affinity

Transformer Decoder Learns from a Pretrained Protein Language Model to Generate Ligands with High Affinity

The drug discovery process can be significantly accelerated by using deep learning methods to suggest molecules with druglike features and, more importantly, that are good candidates to bind specific proteins of interest. We present a novel deep learning generative model, Prot2Drug, that learns to g...

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Bibliographic Details
Main Author: Teresa Maria Creanza (11420282) (author)
Other Authors: Domenico Alberga (1503991) (author), Cosimo Patruno (20627228) (author), Giuseppe Felice Mangiatordi (804889) (author), Nicola Ancona (28781) (author)
Published: 2025
Subjects:
Biophysics
Biochemistry
Molecular Biology
Pharmacology
Infectious Diseases
Biological Sciences not elsewhere classified
Chemical Sciences not elsewhere classified
Information Systems not elsewhere classified
favorable physicochemical properties
drug discovery process
designing molecules binding
bind specific proteins
transformer decoder learns
promising ligands even
suggest molecules
proteins used
significantly accelerated
protein targets
knowledge gathered
high affinity
good candidates
given protein
generate ligands
embedding unveils
druglike features
3d structure
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