Generative network complex (GNC) for drug discovery.
Generative network complex (GNC) for drug discovery.
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DOI：
10.4310/cis.2019.v19.n3.a2
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发表时间：
2019
影响因子：
0.9
通讯作者：
Wei GW
中科院分区：
文献类型：
--
作者：
Grow C;Gao K;Nguyen DD;Wei GW
它仍然是一个具有挑战性的任务，以产生各种各样的新的化合物具有所需的药理学性质。在这项工作中，生成网络复合物（GNC）被提出作为一个新的平台，用于设计新的化合物，预测其物理和化学性质，并选择潜在的候选药物，满足各种可药用标准，如结合亲和力，溶解度，分配系数等。我们联合收割机SMILES字符串生成器，它由编码器，药物性质控制或调节的潜在空间，和解码器组成，利用验证深度神经网络、目标特定三维（3D）姿态生成器和数学深度学习网络，分别生成新化合物、预测其药物特性、构建与目标蛋白质相关的3D姿态以及重新评估可药用性。通过随机输出、受控输出或优化输出，在潜在空间中生成新化合物。在我们的演示中，分别为组织蛋白酶S和BACE靶点生成了208万和280万种新化合物。这些新化合物与种子非常不同，覆盖了更大的化学空间。对于潜在的活性化合物，使用最先进的方法生成它们的3D姿态。由此产生的3D复合物通过基于代数拓扑、微分几何和代数图论的倡导性深度学习算法进一步评估可药用性。在超级计算机上进行，整个过程不到一周。因此，我们的GNC是发现新候选药物的有效新范例。
It remains a challenging task to generate a vast variety of novel compounds with desirable pharmacological properties. In this work, a generative network complex (GNC) is proposed as a new platform for designing novel compounds, predicting their physical and chemical properties, and selecting potential drug candidates that fulfill various druggable criteria such as binding affinity, solubility, partition coefficient, etc. We combine a SMILES string generator, which consists of an encoder, a drug-property controlled or regulated latent space, and a decoder, with verification deep neural networks, a target-specific three-dimensional (3D) pose generator, and mathematical deep learning networks to generate new compounds, predict their drug properties, construct 3D poses associated with target proteins, and reevaluate druggability, respectively. New compounds were generated in the latent space by either randomized output, controlled output, or optimized output. In our demonstration, 2.08 million and 2.8 million novel compounds are generated respectively for Cathepsin S and BACE targets. These new compounds are very different from the seeds and cover a larger chemical space. For potentially active compounds, their 3D poses are generated using a state-of-the-art method. The resulting 3D complexes are further evaluated for druggability by a championing deep learning algorithm based on algebraic topology, differential geometry, and algebraic graph theories. Performed on supercomputers, the whole process took less than one week. Therefore, our GNC is an efficient new paradigm for discovering new drug candidates.