Papers › PSC-CPI: Multi-Scale Protein Sequence-Structure Contrasting for Efficient and...

PSC-CPI: Multi-Scale Protein Sequence-Structure Contrasting for Efficient and Generalizable Compound-Protein Interaction Prediction

13 Feb 2024arXiv:2402.08198archive 2025-07-28

Lirong Wu, Yufei Huang, Cheng Tan, Zhangyang Gao, Bozhen Hu, Haitao Lin, Zicheng Liu, Stan Z. Li

Compound-Protein Interaction (CPI) prediction aims to predict the pattern and strength of compound-protein interactions for rational drug discovery. Existing deep learning-based methods utilize only the single modality of protein sequences or structures and lack the co-modeling of the joint distribution of the two modalities, which may lead to significant performance drops in complex real-world scenarios due to various factors, e.g., modality missing and domain shifting. More importantly, these methods only model protein sequences and structures at a single fixed scale, neglecting more fine-grained multi-scale information, such as those embedded in key protein fragments. In this paper, we propose a novel multi-scale Protein Sequence-structure Contrasting framework for CPI prediction (PSC-CPI), which captures the dependencies between protein sequences and structures through both intra-modality and cross-modality contrasting. We further apply length-variable protein augmentation to allow contrasting to be performed at different scales, from the amino acid level to the sequence level. Finally, in order to more fairly evaluate the model generalizability, we split the test data into four settings based on whether compounds and proteins have been observed during the training stage. Extensive experiments have shown that PSC-CPI generalizes well in all four settings, particularly in the more challenging ``Unseen-Both" setting, where neither compounds nor proteins have been observed during training. Furthermore, even when encountering a situation of modality missing, i.e., inference with only single-modality protein data, PSC-CPI still exhibits comparable or even better performance than previous approaches.

PaperPDFCodeCode Syntology ran

In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

For agents, Syntology's MCP tool lists every function and class Syntology harvested from this paper and whether it ran (how to connect): get_harvested_code_for_paper(arxiv_id="2402.08198")

Code

Syntology Ran 6 of 12 code samples harvested from 1 repository linked to this paper; 6 have no recorded run. Of those that ran: 6 ran with no contract checked.

By repository: official repository: 12 samples from 1 repository, 6 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

lirongwu/psc-cpi officialmentioned in paperpytorchMIT report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

12 samples harvested; 6 ran; 0 honoured the contract we drafted; 6 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

6ran
6unverified

Licence: 0 of the 12 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.

Harvested from lirongwu/psc-cpi. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.

Each sample ends with its code_sha256, Syntology's identity for that exact code. An agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

Repository labels, per sample. official repository: The archive marks this repository official for the paper. named in the paper: The archive records that the paper mentions this repository; it is not marked official. community (archive-listed): In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper. found in paper text by Syntology: Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted. community: Not in the archive's code links for this paper; a community repository Syntology harvested. Samples from a repository marked official are listed first. Licence labels name the repository's licence as recorded at harvest. “Pointer only” means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence label for the reason. File links open the file on GitHub at the default branch, which may have changed since the harvest.

load_test_data lirongwu/psc-cpi/code/dataset.py official repository ran MIT (permissive) · 62050b71a90939d5 · report
load_train_data lirongwu/psc-cpi/code/dataset.py official repository ran MIT (permissive) · 8c6bf9fb947dc45b · report
load_val_data lirongwu/psc-cpi/code/dataset.py official repository ran MIT (permissive) · df364977b3dd312f · report
net_comp_gcn LirongWu/PSC-CPI/code/model.py official repository ran · metamorphic tier: deterministic MIT (permissive) · a20bfac5c8f895fa · report
net_prot_gat LirongWu/PSC-CPI/code/model.py official repository ran · metamorphic tier: deterministic MIT (permissive) · d013c2038d58b73d · report
net_prot_gcn LirongWu/PSC-CPI/code/model.py official repository ran · metamorphic tier: deterministic MIT (permissive) · a9f813a5b779bf7b · report
ProteinEmbed_Model LirongWu/PSC-CPI/code/model.py official repository unverified MIT (permissive) · dea0d167bbce280d · report
cal_affinity_torch lirongwu/psc-cpi/code/utils.py official repository unverified MIT (permissive) · 772e096dc3151918 · report
cal_interaction_torch lirongwu/psc-cpi/code/utils.py official repository unverified MIT (permissive) · cc2cef35a651c681 · report
crossInteraction LirongWu/PSC-CPI/code/model.py official repository unverified MIT (permissive) · 129b91ead96554d8 · report
get_root_logger lirongwu/psc-cpi/code/utils.py official repository unverified MIT (permissive) · 4208189d0b4f5a46 · report
net_prot_sage LirongWu/PSC-CPI/code/model.py official repository unverified MIT (permissive) · 38f5c9dd907c04bc · report

Tasks

Drug Discovery

Results from the paper archive 2025-07-28

No leaderboard rows for this paper in the archive.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections