# CurvFlow-DTA: dual-graph discrete Ricci curvature flow for drug--target affinity prediction

arXiv: https://arxiv.org/abs/2609.22862 · HTML: https://mlsanity.com/paper/2609.22862/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 44 references that are in the map.
- First posted on arXiv 19 Sep 2026.

## Builds on (most cited first, 10 of 10)

- [Strategies for Pre-training Graph Neural Networks](https://mlsanity.com/paper/1905.12265/) (2019)
- [On the Bottleneck of Graph Neural Networks and its Practical Implications](https://mlsanity.com/paper/2006.05205/) (2020)
- [Understanding over-squashing and bottlenecks on graphs via curvature](https://mlsanity.com/paper/2111.14522/) (2021)
- [DeepDTA: Deep Drug-Target Binding Affinity Prediction](https://mlsanity.com/paper/1801.10193/) (2018)
- [Community Detection on Networks with Ricci Flow](https://mlsanity.com/paper/1907.03993/) (2019)
- [Characterizing Complex Networks with Forman-Ricci Curvature and Associated Geometric Flows](https://mlsanity.com/paper/1607.08654/) (2016)
- [GEFA: Early Fusion Approach in Drug-Target Affinity Prediction](https://mlsanity.com/paper/2009.12146/) (2020)
- [DeepGS: Deep Representation Learning of Graphs and Sequences for Drug-Target Binding Affinity Prediction](https://mlsanity.com/paper/2003.13902/) (2020)
- [Deep Graph Convolutional Network and LSTM based approach for predicting drug-target binding affinity](https://mlsanity.com/paper/2201.06872/) (2022)
- [PGraphDTA: Improving Drug Target Interaction Prediction using Protein Language Models and Contact Maps](https://mlsanity.com/paper/2310.04017/) (2023)

## Abstract (arXiv)

Graph neural networks are widely used for drug--target affinity (DTA) prediction, and discrete Ricci curvature has recently been used to characterize molecular graph geometry. Existing curvature-aware DTA approaches mainly use static curvature on the drug graph while representing proteins primarily with sequence-derived features. This leaves pair-adaptive use of graph geometry underexplored, which may limit adaptation to unseen entities in cold-start settings relevant to practical screening. We present CurvFlow-DTA, which replaces a single static curvature representation with weighted Forman curvature flow on both molecular and protein residue--residue contact graphs. A label-independent flow trajectory is precomputed for each entity, and a pair-conditioned selector determines the horizons read by a dual-branch Flow-GINE. A frozen ESM-2 supplies residue-level representations and contact scores used to construct the protein graph. Inference requires only SMILES strings and protein sequences, without a bound complex structure. On Davis and KIBA, CurvFlow-DTA improves on the protocol-matched Ricci-GraphDTA baseline in every warm and cold-start setting. Warm-split mean squared error (MSE) decreases by 19.9% on Davis and 18.9% on KIBA. Across the six cold-start comparisons, MSE decreases by 14.3--27.4%, with higher concordance index (CI) throughout. Within our compiled set of literature baselines, CurvFlow-DTA achieves the lowest MSE on both warm benchmarks and across four out of six cold-start evaluation settings.