We here present AutoGrow4, an open-source program for semi-automated computer-aided drug discovery. AutoGrow4 uses a genetic algorithm to evolve predicted ligands on demand and so is not limited to a virtual library of pre-enumerated compounds. It is a useful tool for generating entirely novel drug-like molecules and for optimizing preexisting ligands. By leveraging recent computational and cheminformatics advancements, AutoGrow4 is faster, more stable, and more modular than previous versions. It implements new docking-program compatibility, chemical filters, multithreading options, and selection methods to support a wide range of user needs. To illustrate both de novo design and lead optimization, we here apply AutoGrow4 to the catalytic domain of poly(ADP-ribose) polymerase 1 (PARP-1), a well characterized DNA-damage-recognition protein. AutoGrow4 produces drug-like compounds with better predicted binding affinities than FDA-approved PARP-1 inhibitors (positive controls). The predicted binding modes of the AutoGrow4 compounds mimic those of the known inhibitors, even when AutoGrow4 is seeded with random small molecules. AutoGrow4 is available under the terms of the Apache License, Version 2.0. A copy can be downloaded free of charge from http://durrantlab.com/autogrow4.

中文翻译：

---

AutoGrow4：用于从头设计和潜在客户优化的开源遗传算法

我们在这里介绍AutoGrow4，这是一个用于半自动计算机辅助药物发现的开源程序。AutoGrow4使用遗传算法按需进化预测的配体，因此不限于预先列举的化合物的虚拟文库。它是生成全新的类药物分子和优化现有配体的有用工具。通过利用最新的计算和化学信息学进步，AutoGrow4比以前的版本更快，更稳定且更具模块化。它实现了新的对接程序兼容性，化学过滤器，多线程选项和选择方法，以支持广泛的用户需求。为了说明从头设计和潜在客户优化，我们在这里将AutoGrow4应用于聚（ADP-核糖）聚合酶1（PARP-1）的催化域，该酶是特征明确的DNA损伤识别蛋白。AutoGrow4产生的药物样化合物具有比FDA批准的PARP-1抑制剂（阳性对照）更好的预测结合亲和力。即使将AutoGrow4植入随机的小分子，AutoGrow4化合物的预测结合模式仍会模仿已知抑制剂的结合模式。根据Apache许可证2.0版的条款，可以使用AutoGrow4。可以从http://durrantlab.com/autogrow4免费下载副本。