# NeuralPLexer3: Accurate Biomolecular Complex Structure Prediction with Flow Models

arXiv: https://arxiv.org/abs/2412.10743 · HTML: https://mlsanity.com/paper/2412.10743/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 66 references that are in the map.
- First posted on arXiv 14 Dec 2024.

## Builds on (most cited first, 20 of 20)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2403.03206/) (2024)
- [Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow](https://mlsanity.com/paper/2209.03003/) (2022)
- [FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness](https://mlsanity.com/paper/2205.14135/) (2022)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [Training Compute-Optimal Large Language Models](https://mlsanity.com/paper/2203.15556/) (2022)
- [Root Mean Square Layer Normalization](https://mlsanity.com/paper/1910.07467/) (2019)
- [Stochastic Interpolants: A Unifying Framework for Flows and Diffusions](https://mlsanity.com/paper/2303.08797/) (2023)
- [Improving and generalizing flow-based generative models with minibatch optimal transport](https://mlsanity.com/paper/2302.00482/) (2023)
- [DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking](https://mlsanity.com/paper/2210.01776/) (2022)
- [Query-Key Normalization for Transformers](https://mlsanity.com/paper/2010.04245/) (2020)
- [EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction](https://mlsanity.com/paper/2202.05146/) (2022)
- [Improving the Training of Rectified Flows](https://mlsanity.com/paper/2405.20320/) (2024)
- [Optimal Flow Matching: Learning Straight Trajectories in Just One Step](https://mlsanity.com/paper/2403.13117/) (2024)
- [State-specific protein-ligand complex structure prediction with a multi-scale deep generative model](https://mlsanity.com/paper/2209.15171/) (2022)
- [RiNALMo: General-Purpose RNA Language Models Can Generalize Well on Structure Prediction Tasks](https://mlsanity.com/paper/2403.00043/) (2024)
- [FlexAttention for Efficient High-Resolution Vision-Language Models](https://mlsanity.com/paper/2407.20228/) (2024)
- [Assessing interaction recovery of predicted protein-ligand poses](https://mlsanity.com/paper/2409.20227/) (2024)

## Built on by (most cited first, 5 of 5)

- [Pearl: A Foundation Model for Placing Every Atom in the Right Location](https://mlsanity.com/paper/2510.24670/) (2025)
- [Matcha: Multi-Stage Riemannian Flow Matching for Accurate and Physically Valid Molecular Docking](https://mlsanity.com/paper/2510.14586/) (2025)
- [TerraBind: Fast and Accurate Binding Affinity Prediction through Coarse Structural Representations](https://mlsanity.com/paper/2602.07735/) (2026)
- [Accurate structural modeling of chemically diverse molecular interfaces with Vilya-2](https://mlsanity.com/paper/2607.25156/) (2026)
- [TurboPairFormer: Fast and Stable Protein Folding Model Training with an Optimized Triangle Attention Kernel](https://mlsanity.com/paper/2610.05854/) (2026)

## Abstract (arXiv)

Structure determination is essential to a mechanistic understanding of diseases and the development of novel therapeutics. Machine-learning-based structure prediction methods have made significant advancements by computationally predicting protein and bioassembly structures from sequences and molecular topology alone. Despite substantial progress in the field, challenges remain to deliver structure prediction models to real-world drug discovery. Here, we present NeuralPLexer3 -- a physics-inspired flow-based generative model that achieves state-of-the-art prediction accuracy on key biomolecular interaction types and improves training and sampling efficiency compared to its predecessors and alternative methodologies. Examined through newly developed benchmarking strategies, NeuralPLexer3 excels in vital areas that are crucial to structure-based drug design, such as physical validity and ligand-induced conformational changes.