论文标题
基于动态分子图的生物物理特性预测实现
Dynamic Molecular Graph-based Implementation for Biophysical Properties Prediction
论文作者
论文摘要
神经网络（GNN）已彻底改变了分子发现，以了解模式并确定可以帮助预测生物物理特性和蛋白质 - 配体相互作用的未知特征。但是，当前模型通常依靠二维分子表示作为输入，而2 \ 3-维结构数据的利用近年来已经获得了当之无愧的吸引力，因为这些模型中的许多模型仍然仅限于静态图表。我们提出了一种基于使用GNN的变压器模型来表征蛋白质 - 配体相互作用的动态特征的新方法。我们的消息传递变压器在一组基于物理模拟的分子动态数据上进行了预训练，以学习坐标结构，并将绑定概率和亲和力预测作为下游任务。通过广泛的测试，我们将结果与现有模型进行了比较，我们的MDA-PLI模型能够以1.2958的RMSE优于分子相互作用预测模型。我们的变压器体系结构和添加时间序列数据启用了几何编码，为这种形式的研究增添了新的维度。
Neural Networks (GNNs) have revolutionized the molecular discovery to understand patterns and identify unknown features that can aid in predicting biophysical properties and protein-ligand interactions. However, current models typically rely on 2-dimensional molecular representations as input, and while utilization of 2\3- dimensional structural data has gained deserved traction in recent years as many of these models are still limited to static graph representations. We propose a novel approach based on the transformer model utilizing GNNs for characterizing dynamic features of protein-ligand interactions. Our message passing transformer pre-trains on a set of molecular dynamic data based off of physics-based simulations to learn coordinate construction and make binding probability and affinity predictions as a downstream task. Through extensive testing we compare our results with the existing models, our MDA-PLI model was able to outperform the molecular interaction prediction models with an RMSE of 1.2958. The geometric encodings enabled by our transformer architecture and the addition of time series data add a new dimensionality to this form of research.
