  Significant interests have recently risen in leveraging sequence-based large
language models (LLMs) for drug design. However, most current applications of
LLMs in drug discovery lack the ability to comprehend three-dimensional (3D)
structures, thereby limiting their effectiveness in tasks that explicitly
involve molecular conformations. In this study, we introduced Token-Mol, a
token-only 3D drug design model. This model encodes all molecular information,
including 2D and 3D structures, as well as molecular property data, into
tokens, which transforms classification and regression tasks in drug discovery
into probabilistic prediction problems, thereby enabling learning through a
unified paradigm. Token-Mol is built on the transformer decoder architecture
and trained using random causal masking techniques. Additionally, we proposed
the Gaussian cross-entropy (GCE) loss function to overcome the challenges in
regression tasks, significantly enhancing the capacity of LLMs to learn
continuous numerical values. Through a combination of fine-tuning and
reinforcement learning (RL), Token-Mol achieves performance comparable to or
surpassing existing task-specific methods across various downstream tasks,
including pocket-based molecular generation, conformation generation, and
molecular property prediction. Compared to existing molecular pre-trained
models, Token-Mol exhibits superior proficiency in handling a wider range of
downstream tasks essential for drug design. Notably, our approach improves
regression task accuracy by approximately 30% compared to similar token-only
methods. Token-Mol overcomes the precision limitations of token-only models and
has the potential to integrate seamlessly with general models such as ChatGPT,
paving the way for the development of a universal artificial intelligence drug
design model that facilitates rapid and high-quality drug design by experts.
