BioNeMo Framework: a modular, high-performance library for AI model development in drug discovery

Artificial Intelligence models encoding biology and chemistry are opening new routes to high-throughput and high-quality in-silico drug development. However, their training increasingly relies on computational scale, with recent protein language models (pLM) training on hundreds of graphical processing units (GPUs). We introduce the BioNeMo Framework to facilitate the training of computational biology and chemistry AI models across hundreds of GPUs. Its modular design allows the integration of individual components, such as data loaders, into existing workflows and is open to community contributions. We detail technical features of the BioNeMo Framework through use cases such as pLM pre-training and fine-tuning. On 256 NVIDIA A100s, BioNeMo Framework trains a three billion parameter BERT-based pLM on over one trillion tokens in 4.2 days. The BioNeMo Framework is open-source and free for everyone to use.

Reinforcement Learning-Driven Linker Design via Fast Attention-based Point Cloud Alignment

Proteolysis-Targeting Chimeras (PROTACs) represent a novel class of small molecules which are designed to act as a bridge between an E3 ligase and a disease-relevant protein, thereby promoting its subsequent degradation. PROTACs are composed of two protein binding "active" domains, linked by a "linker" domain. The design of the linker domain is challenging due to geometric and chemical constraints given by its interactions, and the need to maximize drug-likeness. To tackle these challenges, we introduce ShapeLinker, a method for de novo design of linkers. It performs fragment-linking using reinforcement learning on an autoregressive SMILES generator. The method optimizes for a composite score combining relevant physicochemical properties and a novel, attention-based point cloud alignment score. This new method successfully generates linkers that satisfy both relevant 2D and 3D requirements, and achieves state-of-the-art results in producing novel linkers assuming a target linker conformation. This allows for more rational and efficient PROTAC design and optimization. Code and data are available at https://github.com/aivant/ShapeLinker.