Generative AI for designing and validating easily synthesizable and structurally novel antibiotics: Data and Models

This repository contains data and models used in the following paper. Swanson, K., Liu, G., Catacutan, D., Zou, J. & Stokes, J. Generative AI for designing and validating easily synthesizable and structurally novel antibiotics. Nature Machine Intelligence, 2024. The data and models are meant to be used with the SyntheMol code. More details about how to use the data and models with the code are available here. The Data.zip file has the following structure. Note that the numbers for the Data subdirectories correspond to the supplementary data numbers in the paper (e.g., 1_training_data corresponds to Supplementary Data 1). Data 1_training_data: The Acinetobacter baumannii inhibition data used to train antibiotic property prediction models. 2_chembl: Known antibiotic and antibacterial molecules from ChEMBL, which are used to compute the novelty of generated antibiotic candidates. 4_real_space: Data files and statistics for the Enamine REAL Space. The molecular building blocks file is version 2021 q3-4 while all other REAL Space details are computed from the full enumerated REAL space version 2022 q1-2 (downloaded on August 30, 2022). 5_generations_clogp: Compounds generated by SyntheMol using Chemprop models trained to predict cLogP. 6_generations_chemprop: Compounds generated by SyntheMol using Chemprop models trained to predict A. baumannii inhibition. 7_generations_chemprop_rdkit: Compounds generated by SyntheMol using Chemprop-RDKit models trained to predict A. baumannii inhibition. 8_generations_random_forest: Compounds generated by SyntheMol using random forest models trained to predict A. baumannii inhibition. 9_synthesized: Information on the 58 SyntheMol-generated compounds that were successfully synthesized by Enamine. The Models.zip file contains one folder for each model used in the paper. Note that each model is technically an ensemble of ten individual models, so each directory contains ten model files.本仓库包含了下述论文中使用的数据集与模型。 Swanson, K.、Liu, G.、Catacutan, D.、Zou, J. 与 Stokes, J. 发表的《生成式 AI（Generative AI）用于设计与验证易合成且结构新颖的抗生素》一文，刊载于《自然·机器智能（Nature Machine Intelligence）》，2024年。 本数据集与模型需配合SyntheMol代码使用。有关如何将数据集与该代码结合使用的更多细节，请参见此处。 Data.zip 文件具有如下目录结构。请注意，数据子目录的编号与论文中的补充数据编号一一对应（例如，1_training_data 对应补充数据1）。 Data 1_training_data：用于训练抗生素性质预测模型的鲍曼不动杆菌（Acinetobacter baumannii）抑制活性数据。 2_chembl：来自ChEMBL的已知抗生素与抗菌分子，用于计算生成的抗生素候选化合物的新颖性。 4_real_space：Enamine REAL Space 的数据文件与统计信息。其中分子砌块文件版本为2021年第三至四季度，其余REAL Space相关细节均基于完整枚举版REAL Space 2022年第一至二季度（于2022年8月30日下载）计算得到。 5_generations_clogp：由SyntheMol使用经训练可预测cLogP的Chemprop模型生成的化合物。 6_generations_chemprop：由SyntheMol使用经训练可预测鲍曼不动杆菌抑制活性的Chemprop模型生成的化合物。 7_generations_chemprop_rdkit：由SyntheMol使用经训练可预测鲍曼不动杆菌抑制活性的Chemprop-RDKit模型生成的化合物。 8_generations_random_forest：由SyntheMol使用经训练可预测鲍曼不动杆菌抑制活性的随机森林模型生成的化合物。 9_synthesized：Enamine成功合成的58种SyntheMol生成化合物的相关信息。 Models.zip 文件包含论文中使用的每一种模型对应的独立文件夹。请注意，每个模型本质上均为包含10个独立子模型的集成模型，因此每个目录下均包含10个模型文件。