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MetabolomicsRespectM revealed metabolic heterogeneity powers deep learning for reshaping the DBTL cycle

Synthetic biology, relying on Design-Build-Test-Learn (DBTL) cycle, aims to solve medicine, manufacturing and agriculture problems. However, the DBTL cycle’s Learn (L) step lacks predictive power for the behavior of biological systems, resulting from the incompatibility between sparse testing dat...

2023-06-29 | MTBLS3830 | MetaboLights
MetabolomicsExperimental and computational approaches for deep metabolome annotation with application to the ecotoxicological model organism Daphnia magna

Identifying metabolomes with greater coverage and confidence is a critical step towards interpreting the metabolic basis of human and environmental health. Yet for over two decades the field of metabolomics has been inhibited by limited metabolite identification. To address this, we developed the...

MTBLS2273 | MetaboLights
MetabolomicsWT 2.0: Unveiling the “dark matter” in the metabolome using all-ion stepwise fragmentation acquisition mode (ASFAM) and deep learning-based feature extraction

Current metabolomics methods often miss low-abundance compounds and yield incomplete or ambiguous MS2 spectra, resulting in the presence of “dark matter” within the metabolome. Here, we introduce WT 2.0, which employs an all-ion stepwise fragmentation acquisition mode (ASFAM) to acquire comprehe...

2025-09-23 | MTBLS13023 | MetaboLights
ModelsKim2022 - BayeshERG: A deep learning model for predicting hERG channel blockers
BayeshERG is a predictor of small molecule-induced blockade of the hERG ion channel. To increase its predictive power, the authors pretrained a bayesian graph neural network with 300,000 molecules as a transfer learning exercise. The pretraining set was obtained from Du et al, 2015, and the fine tun...
2024-08-06 | MODEL2408060001 | BioModels
Machine Learning Model FAIR-AIML Ersilia 
ModelsKarim2021 - CardioTox net: Ligand-based prediction of hERG blockade
A robust predictor for hERG channel blockade based on an ensemble of five deep learning models. The authors have collected a dataset from public sources, such as BindingDB and ChEMBL on hERG blockers and non-blockers. The cut-off for hERG blockade was set at IC50 Model Type: Predictive machine lea...
2024-07-18 | MODEL2407180003 | BioModels
Machine Learning Model FAIR-AIML Ersilia 
ModelsWang2022 - Scaffolding protein functional sites using deep learning
Deep learning approaches for scaffolding such functional sites without needing to prespecify the fold or secondary structure of the scaffold. The first approach, “constrained hallucination,” optimizes sequences such that their predicted structures contain the desired functional site. The second appr...
2023-05-10 | BIOMD0000001071 | BioModels
Machine Learning Model FAIR-AIML 
ProteomicsGenerative Deep Learning Pipeline Yields Potent Gram-negative Antibiotics
The escalating crisis of multiresistant bacteria demands the rapid discovery of novel antibiotics that transcend the limitations imposed by the biased chemical space of current libraries. To address this challenge, we introduce an innovative deep learning-driven pipeline for de novo antibiotic desig...
ORGANISM(S): Escherichia coli 
2025-10-06 | PXD066005 | Pride
Machine learning Deep learning Antibiotics Drug discovery Mrsa Automated synthesis De novo drug design Gram-negative 
ProteomicsDeciphering PTM Crosstalk on Hsp90: Integrating Deep Learning for Drug Sensitivity Prediction
Post-translational modification (PTM) of proteins regulates cellular proteostasis by expanding protein functional diversity. This naturally leads to increased proteome complexity as the result of PTM crosstalk. Here, we used a heavily modified molecular chaperone, Heat shock protein-90 (Hsp90), to i...
ORGANISM(S): Homo sapiens (Human) 
2025-09-08 | PXD061927 | Pride
Hsp90 Ai Ganetespib Deep learning Hdac Acetylation Phosphorylation 
ModelsStokes2020 - Antibiotics discovery using deep learning approach.
Based on a simple E.coli growth inhibition assay, the authors trained a model capable of identifying antibiotic potential in compounds structurally divergent from conventional antibiotic drugs. One of the predicted active molecules, Halicin (SU3327), was experimentally validated in vitro and in vivo...
2024-04-22 | MODEL2404080001 | BioModels
Machine Learning Model Ersilia 
ProteomicsMULocDeep: An Interpretable Deep Learning Model for Protein Localization Prediction with Sub-organelle Resolution
Prediction of protein localization plays an important role in understanding protein function and mechanism. A deep learning-based localization prediction tool (“MULocDeep”) assessing each amino acid’s contribution to the localization process provides insights into the mechanism of protein sorting an...
ORGANISM(S): Solanum tuberosum (Potato) Arabidopsis thaliana (Mouse-ear cress) Vicia faba var. faba 
2022-02-15 | PXD019987 | Pride
Protein localization prediction Shotgun proteomics Protein targeting Plant mitochondria isolation Deep learning 
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