Author name cluster

Hannes Stärk

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9 papers

2 author rows

ICLR Conference 2025 Conference Paper

ProtComposer: Compositional Protein Structure Generation with 3D Ellipsoids

Hannes Stärk
Bowen Jing 0002
Tomas Geffner
Jason Yim
Tommi S. Jaakkola
Arash Vahdat
Karsten Kreis

We develop ProtComposer to generate protein structures conditioned on spatial protein layouts that are specified via a set of 3D ellipsoids capturing substructure shapes and semantics. At inference time, we condition on ellipsoids that are hand-constructed, extracted from existing proteins, or from a statistical model, with each option unlocking new capabilities. Hand-specifying ellipsoids enables users to control the location, size, orientation, secondary structure, and approximate shape of protein substructures. Conditioning on ellipsoids of existing proteins enables redesigning their substructure's connectivity or editing substructure properties. By conditioning on novel and diverse ellipsoid layouts from a simple statistical model, we improve protein generation with expanded Pareto frontiers between designability, novelty, and diversity. Further, this enables sampling designable proteins with a helix-fraction that matches PDB proteins, unlike existing generative models that commonly oversample conceptually simple helix bundles. Code is available at https://github.com/NVlabs/protcomposer.

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ICLR Conference 2025 Conference Paper

Think while You Generate: Discrete Diffusion with Planned Denoising

Sulin Liu
Juno Nam
Andrew Campbell
Hannes Stärk
Yilun Xu
Tommi S. Jaakkola
Rafael Gómez-Bombarelli

Discrete diffusion has achieved state-of-the-art performance, outperforming or approaching autoregressive models on standard benchmarks. In this work, we introduce *Discrete Diffusion with Planned Denoising* (DDPD), a novel framework that separates the generation process into two models: a planner and a denoiser. At inference time, the planner selects which positions to denoise next by identifying the most corrupted positions in need of denoising, including both initially corrupted and those requiring additional refinement. This plan-and-denoise approach enables more efficient reconstruction during generation by iteratively identifying and denoising corruptions in the optimal order. DDPD outperforms traditional denoiser-only mask diffusion methods, achieving superior results on language modeling benchmarks such as *text8*, *OpenWebText*, and token-based generation on *ImageNet 256 × 256*. Notably, in language modeling, DDPD significantly reduces the performance gap between diffusion-based and autoregressive methods in terms of generative perplexity. Code is available at [github.com/liusulin/DDPD](https://github.com/liusulin/DDPD).

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ICML Conference 2024 Conference Paper

Dirichlet Flow Matching with Applications to DNA Sequence Design

Hannes Stärk
Bowen Jing 0002
Chenyu Wang 0003
Gabriele Corso
Bonnie Berger
Regina Barzilay
Tommi S. Jaakkola

Discrete diffusion or flow models could enable faster and more controllable sequence generation than autoregressive models. We show that naive linear flow matching on the simplex is insufficient toward this goal since it suffers from discontinuities in the training target and further pathologies. To overcome this, we develop Dirichlet flow matching on the simplex based on mixtures of Dirichlet distributions as probability paths. In this framework, we derive a connection between the mixtures’ scores and the flow’s vector field that allows for classifier and classifier-free guidance. Further, we provide distilled Dirichlet flow matching, which enables one-step sequence generation with minimal performance hits, resulting in $O(L)$ speedups compared to autoregressive models. On complex DNA sequence generation tasks, we demonstrate superior performance compared to all baselines in distributional metrics and in achieving desired design targets for generated sequences. Finally, we show that our classifier-free guidance approach improves unconditional generation and is effective for generating DNA that satisfies design targets.

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NeurIPS Conference 2024 Conference Paper

ET-Flow: Equivariant Flow-Matching for Molecular Conformer Generation

Majdi Hassan
Nikhil Shenoy
Jungyoon Lee
Hannes Stärk
Stephan Thaler
Dominique Beaini

Predicting low-energy molecular conformations given a molecular graph is an important but challenging task in computational drug discovery. Existing state-of-the-art approaches either resort to large scale transformer-based models thatdiffuse over conformer fields, or use computationally expensive methods to gen-erate initial structures and diffuse over torsion angles. In this work, we introduceEquivariant Transformer Flow (ET-Flow). We showcase that a well-designedflow matching approach with equivariance and harmonic prior alleviates the needfor complex internal geometry calculations and large architectures, contrary tothe prevailing methods in the field. Our approach results in a straightforwardand scalable method that directly operates on all-atom coordinates with minimalassumptions. With the advantages of equivariance and flow matching, ET-Flowsignificantly increases the precision and physical validity of the generated con-formers, while being a lighter model and faster at inference. Code is availablehttps: //github. com/shenoynikhil/ETFlow.

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NeurIPS Conference 2024 Conference Paper

Generative Modeling of Molecular Dynamics Trajectories

Bowen Jing
Hannes Stärk
Tommi Jaakkola
Bonnie Berger

Molecular dynamics (MD) is a powerful technique for studying microscopic phenomena, but its computational cost has driven significant interest in the development of deep learning-based surrogate models. We introduce generative modeling of molecular trajectories as a paradigm for learning flexible multi-task surrogate models of MD from data. By conditioning on appropriately chosen frames of the trajectory, we show such generative models can be adapted to diverse tasks such as forward simulation, transition path sampling, and trajectory upsampling. By alternatively conditioning on part of the molecular system and inpainting the rest, we also demonstrate the first steps towards dynamics-conditioned molecular design. We validate the full set of these capabilities on tetrapeptide simulations and show preliminary results on scaling to protein monomers. Altogether, our work illustrates how generative modeling can unlock value from MD data towards diverse downstream tasks that are not straightforward to address with existing methods or even MD itself. Code is available at https: //github. com/bjing2016/mdgen.

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ICML Conference 2024 Conference Paper

Harmonic Self-Conditioned Flow Matching for joint Multi-Ligand Docking and Binding Site Design

Hannes Stärk
Bowen Jing 0002
Regina Barzilay
Tommi S. Jaakkola

A significant amount of protein function requires binding small molecules, including enzymatic catalysis. As such, designing binding pockets for small molecules has several impactful applications ranging from drug synthesis to energy storage. Towards this goal, we first develop HarmonicFlow, an improved generative process over 3D protein-ligand binding structures based on our self-conditioned flow matching objective. FlowSite extends this flow model to jointly generate a protein pocket’s discrete residue types and the molecule’s binding 3D structure. We show that HarmonicFlow improves upon state-of-the-art generative processes for docking in simplicity, generality, and average sample quality in pocket-level docking. Enabled by this structure modeling, FlowSite designs binding sites substantially better than baseline approaches.

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ICLR Conference 2023 Conference Paper

DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking

Gabriele Corso
Hannes Stärk
Bowen Jing 0002
Regina Barzilay
Tommi S. Jaakkola

Predicting the binding structure of a small molecule ligand to a protein---a task known as molecular docking---is critical to drug design. Recent deep learning methods that treat docking as a regression problem have decreased runtime compared to traditional search-based methods but have yet to offer substantial improvements in accuracy. We instead frame molecular docking as a generative modeling problem and develop DiffDock, a diffusion generative model over the non-Euclidean manifold of ligand poses. To do so, we map this manifold to the product space of the degrees of freedom (translational, rotational, and torsional) involved in docking and develop an efficient diffusion process on this space. Empirically, DiffDock obtains a 38% top-1 success rate (RMSD<2A) on PDBBind, significantly outperforming the previous state-of-the-art of traditional docking (23%) and deep learning (20%) methods. Moreover, while previous methods are not able to dock on computationally folded structures (maximum accuracy 10.4%), DiffDock maintains significantly higher precision (21.7%). Finally, DiffDock has fast inference times and provides confidence estimates with high selective accuracy.

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ICML Conference 2022 Conference Paper

3D Infomax improves GNNs for Molecular Property Prediction

Hannes Stärk
Dominique Beaini
Gabriele Corso
Prudencio Tossou
Christian Dallago
Stephan Günnemann
Pietro Liò

Molecular property prediction is one of the fastest-growing applications of deep learning with critical real-world impacts. Although the 3D molecular graph structure is necessary for models to achieve strong performance on many tasks, it is infeasible to obtain 3D structures at the scale required by many real-world applications. To tackle this issue, we propose to use existing 3D molecular datasets to pre-train a model to reason about the geometry of molecules given only their 2D molecular graphs. Our method, called 3D Infomax, maximizes the mutual information between learned 3D summary vectors and the representations of a graph neural network (GNN). During fine-tuning on molecules with unknown geometry, the GNN is still able to produce implicit 3D information and uses it for downstream tasks. We show that 3D Infomax provides significant improvements for a wide range of properties, including a 22% average MAE reduction on QM9 quantum mechanical properties. Moreover, the learned representations can be effectively transferred between datasets in different molecular spaces.

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ICML Conference 2022 Conference Paper

EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction

Hannes Stärk
Octavian Ganea
Lagnajit Pattanaik
Regina Barzilay
Tommi S. Jaakkola

Predicting how a drug-like molecule binds to a specific protein target is a core problem in drug discovery. An extremely fast computational binding method would enable key applications such as fast virtual screening or drug engineering. Existing methods are computationally expensive as they rely on heavy candidate sampling coupled with scoring, ranking, and fine-tuning steps. We challenge this paradigm with EquiBind, an SE(3)-equivariant geometric deep learning model performing direct-shot prediction of both i) the receptor binding location (blind docking) and ii) the ligand’s bound pose and orientation. EquiBind achieves significant speed-ups and better quality compared to traditional and recent baselines. Further, we show extra improvements when coupling it with existing fine-tuning techniques at the cost of increased running time. Finally, we propose a novel and fast fine-tuning model that adjusts torsion angles of a ligand’s rotatable bonds based on closed form global minima of the von Mises angular distance to a given input atomic point cloud, avoiding previous expensive differential evolution strategies for energy minimization.

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