Transformer Based Molecule Encoding for Property Prediction

Synthesis-driven design of 3D molecules for structure-based drug discovery using geometric transformers

Finding drug-like compounds with high bioactivity is essential for drug discovery, but the task is complicated by the high cost of chemical synthesis and validation. With their outstanding performance in de novo drug design, deep generative models represent promising tools for tackling this challenge. In recently years, 3D molecule generative models have gained increasing attention due to their ability to directly utilize the 3D interaction information between the target and ligand. However, it remains challenging to synthesize the molecules generated by these models, limiting the speed of bio…

因果性位置编码：面向非序列特征的Transformer表示学习

Causality-Induced Positional Encoding for Transformer-Based Representation Learning of Non-Sequential Features

针对现有位置编码需预定义特征顺序、不适用于非序列因果特征的问题，提出CAPE方法，通过结构方程建模因果DAG并嵌入双曲空间生成因果感知旋转位置编码，增强Transformer自注意力，经理论与实验验证有效。

G2T-LLM：基于图到树文本编码与微调大语言模型的分子生成方法

G2T-LLM: Graph-to-Tree Text Encoding for Molecule Generation with Fine-Tuned Large Language Models

本文提出G2T-LLM方法，通过图到树文本编码将分子图转换为JSON/XML等层次化格式，并利用监督微调的大语言模型实现基于自然语言提示的分子生成，在多个基准数据集上达到与最先进方法相当的性能。

基于神经网络和Transformer编码器模型的药物性质预测分子编码方法的系统研究

A systematic investigation of molecular encoding methods for drug property predictions across neural network and Transformer encoder-based model

系统研究不同分子编码方法对药物性质预测的影响，使用MLP和MLP+TL模型，发现MACCS和PubChem指纹结合注意力权重可识别关键化学基团，预测准确率平均AUC>0.9。