LIMO: Latent Inceptionism for Targeted Molecule Generation
LIMO: Latent Inceptionism for Targeted Molecule Generation
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DOI：
10.48550/arxiv.2206.09010
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发表时间：
2022-06
期刊：
影响因子：
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通讯作者：
P. Eckmann;Kunyang Sun;Bo Zhao;Mudong Feng;M. Gilson;Rose Yu
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文献类型：
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作者：
P. Eckmann;Kunyang Sun;Bo Zhao;Mudong Feng;M. Gilson;Rose Yu
在药物发现中，产生与靶蛋白具有高结合亲和力的药物样分子仍然是一项困难且资源密集的任务。现有的方法主要采用强化学习、马尔可夫采样或由高斯过程引导的深度生成模型，当生成具有通过计算昂贵的基于物理的方法计算的高结合亲和力的分子时，这些方法可能非常缓慢。我们提出了潜在的分子入侵（LIMO），它显着加速分子生成与入侵类技术。LIMO采用变分自动编码器生成的潜在空间和由两个神经网络依次进行的属性预测，以实现更快的基于梯度的分子属性反向优化。综合实验表明，LIMO在基准任务上具有竞争力，并且在生成具有高结合亲和力的药物样化合物的新任务上明显优于最先进的技术，针对两种蛋白质靶标达到纳摩尔范围。我们用更精确的基于分子动力学的绝对结合自由能计算证实了这些基于对接的结果，并表明我们生成的一种药物样化合物对人类雌激素受体的预测KD（结合亲和力的量度）为6 · 10-14 M，远远超过了典型的早期候选药物和大多数FDA批准的药物对其各自靶点的亲和力。代码可在https://github.com/Rose-STL-Lab/LIMO上获得。
Generation of drug-like molecules with high binding affinity to target proteins remains a difficult and resource-intensive task in drug discovery. Existing approaches primarily employ reinforcement learning, Markov sampling, or deep generative models guided by Gaussian processes, which can be prohibitively slow when generating molecules with high binding affinity calculated by computationally-expensive physics-based methods. We present Latent Inceptionism on Molecules (LIMO), which significantly accelerates molecule generation with an inceptionism-like technique. LIMO employs a variational autoencoder-generated latent space and property prediction by two neural networks in sequence to enable faster gradient-based reverse-optimization of molecular properties. Comprehensive experiments show that LIMO performs competitively on benchmark tasks and markedly outperforms state-of-the-art techniques on the novel task of generating drug-like compounds with high binding affinity, reaching nanomolar range against two protein targets. We corroborate these docking-based results with more accurate molecular dynamics-based calculations of absolute binding free energy and show that one of our generated drug-like compounds has a predicted K D (a measure of binding affinity) of 6 · 10-14 M against the human estrogen receptor, well beyond the affinities of typical early-stage drug candidates and most FDA-approved drugs to their respective targets. Code is available at https://github.com/Rose-STL-Lab/LIMO.