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Swallowing the Bitter Pill: Simplified Scalable Conformer Generation

We present a novel way to predict molecular conformers through a simple formulation that sidesteps many of the
Apple Machine Learning Research 12:24 am on June 3, 2024

Swallowing the Bitter Pill introduces Molecular Conformer Fields (MCF), a novel approach for generating molecular conformations without complex heuristics. MCF simplifies structure learning, scales effectively, and uses diffusion models to predict 3D atomic positions from molecular graphs efficiently, achieving state-of-the-art performance by scaling up model sizes without rotational equivariance constraints.

Novel Approach: Molecular Conformer Fields (MCF) simplifies structure learning and prediction.
Scalability & Generalization: Achieves superior results by scaling up the model without enforcing rotational equivariance.
Data-Efficient Learning: Learns conformer structures directly from molecular graphs, avoiding assumptions about explicit structural models.
Differentiation & Efficiency: Radically simplifies previous methods and eliminates rotational equivariance requirements.
Applicability: The approach is suitable for scientific applications requiring effective structure prediction in complex domains like molecular conformation generation.

https://machinelearning.apple.com/research/swallowing-the-bitter-pill

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Swallowing the Bitter Pill: Simplified Scalable Conformer Generation

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