Yoon
@jyoonlee.bsky.social
Master's @Mila Quebec | Generative Models, AI4Science, ML for Chemistry
🧬5/6 Results (2/2): Chemical Properties
ET-Flow doesn’t just generate molecules—it generates chemically and physically feasible molecules. This makes it highly impactful for downstream applications like drug discovery and materials science.
ET-Flow doesn’t just generate molecules—it generates chemically and physically feasible molecules. This makes it highly impactful for downstream applications like drug discovery and materials science.
December 7, 2024 at 3:46 PM
🧬5/6 Results (2/2): Chemical Properties
ET-Flow doesn’t just generate molecules—it generates chemically and physically feasible molecules. This makes it highly impactful for downstream applications like drug discovery and materials science.
ET-Flow doesn’t just generate molecules—it generates chemically and physically feasible molecules. This makes it highly impactful for downstream applications like drug discovery and materials science.
4/6 Results (1/2): Precision & Speed
ET-Flow achieves state-of-the-art precision in molecular conformer generation even with significantly few inference steps. While raw inference speed trails slightly, recent CUDA kernel optimizations for equivariant architectures will further boost performance.
ET-Flow achieves state-of-the-art precision in molecular conformer generation even with significantly few inference steps. While raw inference speed trails slightly, recent CUDA kernel optimizations for equivariant architectures will further boost performance.
December 7, 2024 at 3:46 PM
4/6 Results (1/2): Precision & Speed
ET-Flow achieves state-of-the-art precision in molecular conformer generation even with significantly few inference steps. While raw inference speed trails slightly, recent CUDA kernel optimizations for equivariant architectures will further boost performance.
ET-Flow achieves state-of-the-art precision in molecular conformer generation even with significantly few inference steps. While raw inference speed trails slightly, recent CUDA kernel optimizations for equivariant architectures will further boost performance.
We’re excited to present ET-Flow at #NeurIPS 2024—an Equivariant Flow Matching model that combines simplicity, efficiency, and precision to set a new standard for 3D molecular conformer generation.
🔖Paper: arxiv.org/abs/2410.22388
🔗Github: github.com/shenoynikhil...
🔖Paper: arxiv.org/abs/2410.22388
🔗Github: github.com/shenoynikhil...
December 7, 2024 at 3:39 PM
We’re excited to present ET-Flow at #NeurIPS 2024—an Equivariant Flow Matching model that combines simplicity, efficiency, and precision to set a new standard for 3D molecular conformer generation.
🔖Paper: arxiv.org/abs/2410.22388
🔗Github: github.com/shenoynikhil...
🔖Paper: arxiv.org/abs/2410.22388
🔗Github: github.com/shenoynikhil...
🧬5/6 Results (2/2): Chemical Properties
ET-Flow doesn’t just generate molecules—it generates chemically and physically feasible molecules. This makes it highly impactful for downstream applications like drug discovery and materials science.
ET-Flow doesn’t just generate molecules—it generates chemically and physically feasible molecules. This makes it highly impactful for downstream applications like drug discovery and materials science.
December 7, 2024 at 3:36 PM
🧬5/6 Results (2/2): Chemical Properties
ET-Flow doesn’t just generate molecules—it generates chemically and physically feasible molecules. This makes it highly impactful for downstream applications like drug discovery and materials science.
ET-Flow doesn’t just generate molecules—it generates chemically and physically feasible molecules. This makes it highly impactful for downstream applications like drug discovery and materials science.
📊4/6 Results(1/2): Precision & Speed
ET-Flow achieves state-of-the-art precision in molecular conformer generation with significantly few inference steps. While raw inference speed trails slightly, recent CUDA kernel optimizations for equivariant architectures will further boost performance.
ET-Flow achieves state-of-the-art precision in molecular conformer generation with significantly few inference steps. While raw inference speed trails slightly, recent CUDA kernel optimizations for equivariant architectures will further boost performance.
December 7, 2024 at 3:36 PM
📊4/6 Results(1/2): Precision & Speed
ET-Flow achieves state-of-the-art precision in molecular conformer generation with significantly few inference steps. While raw inference speed trails slightly, recent CUDA kernel optimizations for equivariant architectures will further boost performance.
ET-Flow achieves state-of-the-art precision in molecular conformer generation with significantly few inference steps. While raw inference speed trails slightly, recent CUDA kernel optimizations for equivariant architectures will further boost performance.