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A framework for conditional diffusion modelling with applications in motif scaffolding for protein design
Dec 14, 2023
Many protein design applications, such as binder or enzyme design, require scaffolding a structural motif with high precision. Generative modelling paradigms based on denoising diffusion processes emerged as a leading candidate to address this motif scaffolding problem and have shown early experimental success in some cases. In the diffusion paradigm, motif scaffolding is treated as a conditional generation task, and several conditional generation protocols were proposed or imported from the Computer Vision literature. However, most of these protocols are motivated heuristically, e.g. via analogies to Langevin dynamics, and lack a unifying framework, obscuring connections between the different approaches. In this work, we unify conditional training and conditional sampling procedures under one common framework based on the mathematically well-understood Doob's h-transform. This new perspective allows us to draw connections between existing methods and propose a new variation on existing conditional training protocols. We illustrate the effectiveness of this new protocol in both, image outpainting and motif scaffolding and find that it outperforms standard methods.
On How AI Needs to Change to Advance the Science of Drug Discovery
Dec 23, 2022
Research around AI for Science has seen significant success since the rise of deep learning models over the past decade, even with longstanding challenges such as protein structure prediction. However, this fast development inevitably made their flaws apparent -- especially in domains of reasoning where understanding the cause-effect relationship is important. One such domain is drug discovery, in which such understanding is required to make sense of data otherwise plagued by spurious correlations. Said spuriousness only becomes worse with the ongoing trend of ever-increasing amounts of data in the life sciences and thereby restricts researchers in their ability to understand disease biology and create better therapeutics. Therefore, to advance the science of drug discovery with AI it is becoming necessary to formulate the key problems in the language of causality, which allows the explication of modelling assumptions needed for identifying true cause-effect relationships. In this attention paper, we present causal drug discovery as the craft of creating models that ground the process of drug discovery in causal reasoning.