Univ. Lille, Inserm, Institut Pasteur de Lille, U1177-Drugs and Molecules for Living Systems, Lille F-59000, France.
Bioinformatics. 2020 Aug 15;36(14):4225-4226. doi: 10.1093/bioinformatics/btaa494.
Several web-based tools predict the putative targets of a small molecule query compound by similarity to molecules with known bioactivity data using molecular fingerprints. In numerous situations, it would however be valuable to be able to run such computations on a local computer. We present FastTargetPred, a new program for the prediction of protein targets for small molecule queries. Structural similarity computations rely on a large collection of confirmed protein-ligand activities extracted from the curated ChEMBL 25 database. The program allows to annotate an input chemical library of ∼100k compounds within a few hours on a simple personal computer.
FastTargetPred is written in Python 3 (≥3.7) and C languages. Python code depends only on the Python Standard Library. The program can be run on Linux, MacOS and Windows operating systems. Pre-compiled versions are available at https://github.com/ludovicchaput/FastTargetPred. FastTargetPred is licensed under the GNU GPLv3. The program calls some scripts from the free chemistry toolkit MayaChemTools.
Supplementary data are available at Bioinformatics online.
有几种基于网络的工具可通过小分子查询化合物与具有已知生物活性数据的分子的相似性，使用分子指纹来预测小分子查询化合物的潜在靶标。然而，在许多情况下，能够在本地计算机上运行此类计算将是非常有价值的。我们介绍了 FastTargetPred，这是一种用于预测小分子查询的蛋白质靶标的新程序。结构相似性计算依赖于从经过精心整理的 ChEMBL 25 数据库中提取的大量已确认的蛋白质-配体活性。该程序允许在简单的个人计算机上在几个小时内注释输入的化学库中约 100k 个化合物。
FastTargetPred 是用 Python 3（≥3.7）和 C 语言编写的。Python 代码仅依赖于 Python 标准库。该程序可以在 Linux、MacOS 和 Windows 操作系统上运行。预编译版本可在 https://github.com/ludovicchaput/FastTargetPred 上获得。FastTargetPred 遵循 GNU GPLv3 许可证。该程序调用了免费化学工具包 MayaChemTools 中的一些脚本。
补充数据可在 Bioinformatics 在线获得。
