  Generating novel active molecules for a given protein is an extremely
challenging task for generative models that requires an understanding of the
complex physical interactions between the molecule and its environment. In this
paper, we present a novel generative model, BindGPT which uses a conceptually
simple but powerful approach to create 3D molecules within the protein's
binding site. Our model produces molecular graphs and conformations jointly,
eliminating the need for an extra graph reconstruction step. We pretrain
BindGPT on a large-scale dataset and fine-tune it with reinforcement learning
using scores from external simulation software. We demonstrate how a single
pretrained language model can serve at the same time as a 3D molecular
generative model, conformer generator conditioned on the molecular graph, and a
pocket-conditioned 3D molecule generator. Notably, the model does not make any
representational equivariance assumptions about the domain of generation. We
show how such simple conceptual approach combined with pretraining and scaling
can perform on par or better than the current best specialized diffusion
models, language models, and graph neural networks while being two orders of
magnitude cheaper to sample.
