Spotlight: Montai Builds a Multimodal AI Platform for Drug Discovery Using NVIDIA NIM Microservices

[jwitsoe]

Originally published at: https://developer.nvidia.com/blog/spotlight-montai-builds-a-multimodal-ai-platform-for-drug-discovery-using-nvidia-nim-microservices/

Drug discovery aims to develop new therapeutic agents that effectively target diseases while minimizing side effects for patients. Using multimodal data—such as molecular structures, cellular images, sequences, and unstructured data—can be highly valuable in identifying novel and safe drug candidates.  Yet, creating multimodal AI models for computer-aided drug discovery is challenging. These models must align…

[teamardigen]

@jwitsoe

Currently, the collaborative efforts are focused on incorporating a fifth modality, the “docking fingerprint,” derived from DiffDock predictions. The role of NVIDIA BioNeMo has been instrumental in scaling up the inference process, enabling more efficient computation. For example, DiffDock on the DUD-E dataset, with 40 poses per ligand on eight NVIDIA A100 Tensor Core GPUs, achieves a processing speed of 0.76 seconds per ligand.

These advancements underscore the importance of efficient GPU utilization in drug screening and highlight the successful use of NVIDIA NIM and a multimodal AI model. The collaboration between Montai and NVIDIA represents a critical step forward in the pursuit of more effective and efficient drug discovery processes.

That’s an impressive advancement! Incorporating the docking fingerprint as a fifth modality and leveraging NVIDIA BioNeMo for large-scale inference showcases how AI and GPU acceleration can revolutionize drug discovery. The collaboration between Montai and NVIDIA highlights the power of computational efficiency and multimodal integration in identifying promising drug candidates faster and more accurately.