Deep learning has demonstrated promising results in de novo drug design. Often, the general pipeline consists of training a generative model (G) to learn the building rules of valid molecules, then using a biassing technique such as reinforcement learning (RL) to focus G on the desired chemical space. However, this sequential training of the same model for different tasks is known to be prone to a catastrophic forgetting (CF) phenomenon. This work presents a novel yet simple approach to bias G with significantly less CF than RL. The proposed method relies on backpropagating a reduced value of the cross-entropy loss used to train G according to the proportion of desired molecules that the biased-G can generate. We named our approach CRLV, short for conditional reduction of the loss value. We compared the two biased models (RL-biased-G and CRLV-biased-G) for four different objectives related to de novo drug design. CRLV-biased-G outperformed RL-biased-G in all four objectives and manifested appreciably less CF. Besides, an intersection analysis between molecules generated by the RL-biased-G and the CRLV-biased-G revealed that they can be used jointly without losing diversity given the low percentage of overlap between the two to further increase the desirability. Finally, we show that the difficulty of an objective is proportional to (i) its frequency in the dataset used to train G and (ii) the associated structural variance (SV), which is a new parameter we introduced in this paper, calling for novel exploration techniques for such difficult objectives.

中文翻译：

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有条件地减少损失值与强化学习对从头药物设计生成器的偏差

深度学习在从头药物设计中表现出了有希望的结果。通常，一般流程包括训练生成模型 (G) 以学习有效分子的构建规则，然后使用强化学习 (RL) 等偏置技术将 G 集中在所需的化学空间上。然而，众所周知，针对不同任务对同一模型进行连续训练很容易出现灾难性遗忘（CF）现象。这项工作提出了一种新颖而简单的偏置 G 方法，其 CF 明显小于 RL。所提出的方法依赖于根据偏置 G 可以生成的所需分子的比例反向传播用于训练 G 的交叉熵损失的减小值。我们将我们的方法命名为 CRLV，是有条件减少损失值的缩写。我们比较了与从头药物设计相关的四个不同目标的两种偏倚模型（RL-biased-G 和 CRLV-biased-G）。CRLV-biased-G 在所有四个目标中均优于 RL-biased-G，并且表现出明显较少的 CF。此外，RL-biased-G 和 CRLV-biased-G 生成的分子之间的交叉分析表明，鉴于两者之间的重叠百分比较低，它们可以联合使用而不会失去多样性，以进一步提高需求。最后，我们表明目标的难度与（i）用于训练 G 的数据集中的频率和（ii）相关的结构方差（SV）成正比，这是我们在本文中引入的一个新参数，要求针对此类困难目标的新颖探索技术。