TorchDrug is a robust machine learning platform designed specifically for drug discovery, incorporating advanced techniques such as graph machine learning (including graph neural networks and geometric deep learning), deep generative models, and reinforcement learning. It aims to facilitate rapid prototyping of drug discovery models using PyTorch while reducing the need for extensive domain knowledge. Key features include accessible building blocks for fast ideas iteration, comprehensive benchmarks for systematic comparison of models, and the ability to scale training and inference across multiple CPUs, GPUs, or distributed settings. TorchDrug supports a variety of drug discovery tasks such as property prediction, de novo molecule design, reaction prediction, and biomedical knowledge graph reasoning.
• minimal domain knowledge
• datasets and building blocks
• comprehensive benchmarks
• scalable training and inference
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