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Valence Labs

@valence_ai • 6,328 subscribers

Decoding biology to radically improve lives. Décrypter la biologie pour radicalement améliorer les vies. Powered by | Propulsé par @recursionpharma.

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Physics-based simulation tools like DFT and FEP are being repurposed for dataset development. Once manually run and reviewed by experts, these tools are now producing tens of millions of synthetic data points without close human oversight. These techniques are not without their issues. Studies have shown that in certain computational datasets as much as 58% of DFT generated data is not physically possible. In Ihor's op-ed, he describes several cheap and effective methods of avoiding subtle failure modes in physics simulation as a reminder to be careful with these methods at scale. Op-ed: Ihor Neporozhnii 🇺🇦

Physics-based simulation tools like DFT and FEP are being repurposed for dataset development. Once manually run and reviewed by experts, these tools are now producing tens of millions of synthetic data points without close human oversight. These techniques are not without their issues. Studies have shown that in certain computational datasets as much as 58% of DFT generated data is not physically possible. In Ihor's op-ed, he describes several cheap and effective methods of avoiding subtle failure modes in physics simulation as a reminder to be careful with these methods at scale. Op-ed: Ihor Neporozhnii 🇺🇦

10,540 просмотров • 1 месяц назад

Our bodies’ molecules aren’t static so why do many AI models treat them that way? Without considering the physical movement of molecules, many AI tools fail to robustly capture what makes drug candidates promising. AquaGen is a generative AI framework built to close that gap: an all-atom, explicit-solvent model that runs an order of magnitude faster than traditional MD methods. We’re excited about the implications of this work on early-stage drug discovery. Technical report and blog below.

Our bodies’ molecules aren’t static so why do many AI models treat them that way? Without considering the physical movement of molecules, many AI tools fail to robustly capture what makes drug candidates promising. AquaGen is a generative AI framework built to close that gap: an all-atom, explicit-solvent model that runs an order of magnitude faster than traditional MD methods. We’re excited about the implications of this work on early-stage drug discovery. Technical report and blog below.

10,460 просмотров • 3 месяцев назад

Introducing SAFE, Sequential Attachment-based Fragment Embedding, a novel molecular line notation that redefines molecular design tasks as simple autoregressive sequence generation. 🧵

Introducing SAFE, Sequential Attachment-based Fragment Embedding, a novel molecular line notation that redefines molecular design tasks as simple autoregressive sequence generation. 🧵

12,856 просмотров • 2 лет назад

Introducing a small portion of proprietary phenomics data from Recursion into the training of MolGPS, our foundation model for molecular property prediction, led to massive performance increases on new & existing benchmarks. Learn more about the impact of data & scale.

Introducing a small portion of proprietary phenomics data from Recursion into the training of MolGPS, our foundation model for molecular property prediction, led to massive performance increases on new & existing benchmarks. Learn more about the impact of data & scale.

10,100 просмотров • 2 лет назад

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