Explainable AI for Molecules - AiChemist

Informazioni relative al progetto

AiChemist

ID dell’accordo di sovvenzione: 101120466

DOI

10.3030/101120466

Data della firma CE 14 Luglio 2023

Data di avvio 1 Settembre 2023

Data di completamento 31 Agosto 2027

Finanziato da

• Marie Skłodowska-Curie Actions (MSCA)

Costo totale

Nessun dato

Contributo UE

€ 3 028 356,00

Coordinato da

HELMHOLTZ ZENTRUM MUENCHEN DEUTSCHES FORSCHUNGSZENTRUM FUER GESUNDHEIT UND UMWELT GMBH
Germany

CORDIS fornisce collegamenti ai risultati finali pubblici e alle pubblicazioni dei progetti ORIZZONTE.

I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .

Risultati finali

Documents, reports (6)

Minutes of the kick-off meeting

Kick-off meeting is organised

Communication and public engagement

Report summarises the communications and public engagement activities with a goal to identify potential gaps (if any)

WS2 school

School report

WS1 school

School report

Cardiotoxicity modelling

Development of explainable model for cardiotoxicity used as a common benchmark

XAI approaches for toxicity

Overview of XAI methods which are consistent with human interpretation of toxicity

Websites, patent fillings, videos etc. (5)

Advertisement of positions

Positions are advertised

First newsletter

Newsletter reviewing recruitment is published

Applications started

Positions are announced and web site contains information on how to apply for all PhDs

Website

Web site of the project is publicly available

SS1 school

School report

Pubblicazioni

Peer reviewed articles (5)

Using test-time augmentation to investigate explainable AI: inconsistencies between method, model and human intuition

Autori: Peter B. R. Hartog, Fabian Krüger, Samuel Genheden, Igor V. Tetko
Pubblicato in: Journal of Cheminformatics, Numero 16, 2024, ISSN 1758-2946
Editore: Springer Science and Business Media LLC
DOI: 10.1186/S13321-024-00824-1

NanoToxRadar: A Multitarget Nano-QSAR Model for Predicting the Cytotoxicity Values of Multicomponent Nanoparticles

Autori: Jaehyeon Park, Shahzad Rashid, Helena Copsey, Lang Tran, Alex Zabeo, Danail Hristozov, Giorgos P. Gakis, Costas Charitidis, Seokjoo Yoon, Hyun Kil Shin
Pubblicato in: ACS Nanoscience Au, Numero 5, 2025, ISSN 2694-2496
Editore: American Chemical Society (ACS)
DOI: 10.1021/ACSNANOSCIENCEAU.5C00035

The openOCHEM consensus model is the best-performing open-source predictive model in the First EUOS/SLAS joint compound solubility challenge

Autori: Andrea Hunklinger, Peter Hartog, Martin Šícho, Guillaume Godin, Igor V. Tetko
Pubblicato in: SLAS Discovery, Numero 29, 2024, ISSN 2472-5552
Editore: Elsevier BV
DOI: 10.26434/CHEMRXIV-2023-P8QCV

Predicting reaction conditions: a data-driven perspective

Autori: Matthew Ball, Dragos Horvath, Thierry Kogej, Mikhail Kabeshov, Alexandre Varnek
Pubblicato in: Chemical Science, Numero 16, 2025, ISSN 2041-6520
Editore: Royal Society of Chemistry (RSC)
DOI: 10.26434/CHEMRXIV-2025-VC6TV

MolEncoder: towards optimal masked language modeling for molecules

Autori: Fabian P. Krüger, Nicklas Österbacka, Mikhail Kabeshov, Ola Engkvist, Igor Tetko
Pubblicato in: Digital Discovery, 2025, ISSN 2635-098X
Editore: Royal Society of Chemistry (RSC)
DOI: 10.1039/D5DD00369E

Other (3)

Tox24 Challenge

Autori: Tetko, Igor V.
Pubblicato in: Chemical Research in Toxicology, 2024
DOI: 10.1021/ACS.CHEMRESTOX.4C00192

Advanced machine learning for innovative drug discovery

Autori: Igor V. Tetko; Djork-Arné Clevert
Pubblicato in: Journal of Cheminformatics, 2025
DOI: 10.1186/S13321-025-01061-W

Advancing Human and Environmental Safety Science Using <i>In Silico</i> Methods

Autori: Alessandra Roncaglioni, Simona Kovarich, Kamel Mansouri, Igor V. Tetko
Pubblicato in: Chemical Research in Toxicology, Numero 38, 2025, ISSN 0893-228X
Editore: American Chemical Society (ACS)
DOI: 10.1021/ACS.CHEMRESTOX.5C00293

Conference proceedings (2)

Uni-Mol Docking V2: Towards Realistic and Accurate Binding Pose Prediction

Autori: Eric Alcaide, Zhifeng Gao, Guolin Ke, Yaqi Li, Linfeng Zhang, Hang Zheng, Gengmo Zhou
Pubblicato in: Lecture Notes in Computer Science, Artificial Neural Networks and Machine Learning. ICANN 2025 International Workshops and Special Sessions, 2025
Editore: Springer Nature Switzerland
DOI: 10.48550/ARXIV.2405.11769

MolEncoder: Improved Masked Language Modeling for Molecules

Autori: Fabian P. Krüger, Nicklas Österbacka, Mikhail Kabeshov, Ola Engkvist, Igor Tetko
Pubblicato in: Lecture Notes in Computer Science, Artificial Neural Networks and Machine Learning. ICANN 2025 International Workshops and Special Sessions, 2025
Editore: Springer Nature Switzerland
DOI: 10.26434/CHEMRXIV-2025-H4W9D