Small molecules play a pivotal role in modern medicine, and scrutinizing
their interactions with protein targets is essential for the discovery and
development of novel, life-saving therapeutics. The term "bioactivity"
encompasses various biological effects resulting from these interactions,
including both binding and functional responses. The magnitude of bioactivity
dictates the therapeutic or toxic pharmacological outcomes of small molecules,
rendering accurate bioactivity prediction crucial for the development of safe
and effective drugs. However, existing structural datasets of small
molecule-protein interactions are often limited in scale and lack
systematically organized bioactivity labels, thereby impeding our understanding
of these interactions and precise bioactivity prediction. In this study, we
introduce a comprehensive dataset of small molecule-protein interactions,
consisting of over a million binding structures, each annotated with real
biological activity labels. This dataset is designed to facilitate unbiased
bioactivity prediction. We evaluated several classical models on this dataset,
and the results demonstrate that the task of unbiased bioactivity prediction is
challenging yet essential.

这项研究介绍了一个包含超过一百万个结合结构及其真实生物活性标签的小分子 - 蛋白质相互作用全面数据集，用于推动无偏生物活性预测，并评估了几种经典模型在该数据集上的效果，结果表明无偏生物活性预测任务既具有挑战性又是必不可少的。

SIU: 百万级结构化小分子 - 蛋白质相互作用数据集用于无偏生物活性预测

SIU: A Million-Scale Structural Small Molecule-Protein Interaction  Dataset for Unbiased Bioactivity Prediction

Advances in deep generative models shed light on de novo molecule generation
with desired properties. However, molecule generation targeted for dual protein
targets still faces formidable challenges including protein 3D structure data
requisition for model training, auto-regressive sampling, and model
generalization for unseen targets. Here, we proposed DiffDTM, a novel
conditional structure-free deep generative model based on a diffusion model for
dual targets based molecule generation to address the above issues.
Specifically, DiffDTM receives protein sequences and molecular graphs as inputs
instead of protein and molecular conformations and incorporates an information
fusion module to achieve conditional generation in a one-shot manner. We have
conducted comprehensive multi-view experiments to demonstrate that DiffDTM can
generate drug-like, synthesis-accessible, novel, and high-binding affinity
molecules targeting specific dual proteins, outperforming the state-of-the-art
(SOTA) models in terms of multiple evaluation metrics. Furthermore, we utilized
DiffDTM to generate molecules towards dopamine receptor D2 and
5-hydroxytryptamine receptor 1A as new antipsychotics. The experimental results
indicate that DiffDTM can be easily plugged into unseen dual targets to
generate bioactive molecules, addressing the issues of requiring insufficient
active molecule data for training as well as the need to retrain when
encountering new targets.

本文提出了 DiffDTM，一种基于扩散模型的条件无结构深度生成模型，将蛋白质序列和分子图作为输入，生成针对特定双蛋白的药物，实现了一次条件样本生成。在多个评估指标方面优于现有技术模型， 可以轻松地应用于新的双蛋白目标，生成具有生物活性的分子。