Dataset supporting the article 'Transformer-decoder GPT models for generating virtual screening libraries of HMGCR inhibitors: effects of temperature, prompt-length and transfer-learning strategies'

How to cite this Dataset

Cafiero, Mauricio (2024): Dataset supporting the article 'Transformer-decoder GPT models for generating virtual screening libraries of HMGCR inhibitors: effects of temperature, prompt-length and transfer-learning strategies'. University of Reading. Dataset. https://doi.org/10.17864/1947.001340

Description

Raw data for virtual screeing libraries generated by a generative, pre-trained transformer-decoder model. Models were pre-trained on a general drug database from ZINC15, and fine-tuned on inhibitors of HMGCR from ChEMBL. Libraries used different transfer-learning strategies, different prompt-lengths and different temperatures. The resultant libraries were screened against a deep neural network trained on experimental HMGCR IC50 values to predict IC50 values, docking scores from Autodock Vina, quantitative estimate of drug-likeness, Tanimoto similarity to known statin drugs, and other properties. This dataset contains tables of properties as well as CSV files with the generated libraries, a TKinter-based GUI to interacting with the library, and docking poses for selected molecules.

Resource Type:

Dataset

Creators:

Cafiero, Mauricio