Drug discovery projects entail cycles of design, synthesis, and testing that
yield a series of chemically related small molecules whose properties, such as
binding affinity to a given target protein, are progressively tailored to a
particular drug discovery goal. The use of deep learning technologies could
augment the typical practice of using human intuition in the design cycle, and
thereby expedite drug discovery projects. Here we present DESMILES, a deep
neural network model that advances the state of the art in machine learning
approaches to molecular design. We applied DESMILES to a previously published
benchmark that assesses the ability of a method to modify input molecules to
inhibit the dopamine receptor D2, and DESMILES yielded a 77% lower failure rate
compared to state-of-the-art models. To explain the ability of DESMILES to hone
molecular properties, we visualize a layer of the DESMILES network, and further
demonstrate this ability by using DESMILES to tailor the same molecules used in
the D2 benchmark test to dock more potently against seven different receptors.

该论文介绍了一种基于深度学习技术的神经网络模型 DESMILES，其可以用于药物发现中的分子设计，实验结果表明 DESMILES 比现有模型更有效地改进了输入分子，降低了失败率，并可用于修改分子以促进其在不同受体中的结合亲和力。

用于促进药物发现的化学空间的深度学习视角

A deep-learning view of chemical space designed to facilitate drug  discovery

A major challenge in computational chemistry is the generation of novel
molecular structures with desirable pharmacological and physiochemical
properties. In this work, we investigate the potential use of autoencoder, a
deep learning methodology, for de novo molecular design. Various generative
autoencoders were used to map molecule structures into a continuous latent
space and vice versa and their performance as structure generator was assessed.
Our results show that the latent space preserves chemical similarity principle
and thus can be used for the generation of analogue structures. Furthermore,
the latent space created by autoencoders were searched systematically to
generate novel compounds with predicted activity against dopamine receptor type
2 and compounds similar to known active compounds not included in the training
set were identified.

本文探讨如何使用深度学习方法 - 自动编码器，进行新型药物分子结构的设计，并通过搜索自动生成的 latent space, 生成预测活性的新化合物，并识别与训练集不同但具有相似效应的活性化合物。