Row 12567

Row ID: 12567 | Dataset Entry | Axioma AXP Content Repository

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One of the papers cited by AlpahFold3 for their modeling choices is this one - https://arxiv.org/pdf/2311.17932 . That link goes to an earlier version of "Generating Molecular Conformer Fields" which, unlike the more recent version, says the following:

> Instead of using Graph Neural Networks with intricate equivariance designs, MCF builds a score network using PerceiverIO (Jaegle et al., 2022) (see Fig. 1) which is a scalable and efficient variant of the Transformer architecture. Our model is simple to implement and efficient to scale. Experiments on recent conformer generation benchmarks show MCF surpasses strong baselines by a gap that gets larger as we scale model capacity

Basically, they deliberately avoid using equivariance in order to simplify their model, and they compensate for this by making their model really big. AlphaFold3 is based on the same logic, but they don't explicitly mention that they don't need equivariance because they use a larger scale model.

If you want to do single GPU training then it might be necessary to bake a lot more symmetries into the model, which might make the model design subtle or complicated.

cc u/Exarctus

FieldValue
text One of the papers cited by AlpahFold3 for their modeling choices is this one - https://arxiv.org/pdf/2311.17932 . That link goes to an earlier version of "Generating Molecular Conformer Fields" which, unlike the more recent version, says the following: > Instead of using Graph Neural Networks with intricate equivariance designs, MCF builds a score network using PerceiverIO (Jaegle et al., 2022) (see Fig. 1) which is a scalable and efficient variant of the Transformer architecture. Our model is …
label r/machinelearning
dataType comment
communityName r/MachineLearning
datetime 2024-05-20
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  "text": "One of the papers cited by AlpahFold3 for their modeling choices is this one - https://arxiv.org/pdf/2311.17932 . That link goes to an earlier version of \"Generating Molecular Conformer Fields\" which, unlike the more recent version, says the following:\n\n> Instead of using Graph Neural Networks with intricate equivariance designs, MCF builds a score network using PerceiverIO (Jaegle et al., 2022) (see Fig. 1) which is a scalable and efficient variant of the Transformer architecture. Our model is simple to implement and efficient to scale. Experiments on recent conformer generation benchmarks show MCF surpasses strong baselines by a gap that gets larger as we scale model capacity\n\nBasically, they deliberately avoid using equivariance in order to simplify their model, and they compensate for this by making their model really big. AlphaFold3 is based on the same logic, but they don't explicitly mention that they don't need equivariance because they use a larger scale model.\n\nIf you want to do single GPU training then it might be necessary to bake a lot more symmetries into the model, which might make the model design subtle or complicated.\n\ncc u/Exarctus",
  "label": "r/machinelearning",
  "dataType": "comment",
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  "datetime": "2024-05-20",
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