Real-world examples showcasing how MolDrug’s computational methods have been applied
AIM 🎯
To predict ADME properties before they become development bottlenecks in drug development.
METHODOLOGY ⚙️
ProtoADME is built on validated QSAR models developed from curated experimental datasets. The platform integrates multiple machine-learning approaches to estimate absorption, distribution, metabolism and excretion endpoints, delivering fast, reproducible and regulatory-oriented results. All predictions are accompanied by QMRF and QPRF documentation, ensuring transparency, interpretability and suitability for regulatory and decision-support contexts.
OUTCOME 📈
Supports early compound prioritization and hit-to-lead optimization by identifying ADME liabilities before costly experimental stages
RELATED PROJECTS 🔗
ProtoADME, DIMERKSTOP, …
AIM 🎯
Identification of small ligands with the potential to target cancer-associated G-quadruplex (G4) DNA
METHODOLOGY ⚙️
A novel approach was developed by integrating validated multi-target QSAR models and in-house ADMET models as a knowledgebase, which was subsequently used to screen potential MTDLs. The selectivity and binding characteristics of the screened ligands toward G4s over duplex DNA were analyzed using in silico and in vitro assays.
OUTCOME 📈
Selection of preclinical cantidates selected by virtual screening of large chemical databases , followed by evaluation of screened ligands through molecular docking, molecular dynamics simulations, and key in vitro assays.
RELATED PROJECTS 🔗
G4-mtQSAR
AIM 🎯
Study the metabolic impact of COVID-19 on RBCs, taking into account disease severity, and major risk factors.
METHODOLOGY ⚙️
Metabolomic profiles of RBCs from healthy controls and patients with COVID-19 were obtained by Nuclear Magnetic Resonance. Multivariate and univariate statistical analyses of RBC metabolomic data were performed across sex-specific cohorts. Various clinical factors, such as body mass index, comorbidities, and critical clinical status, were also considered.
OUTCOME 📈
COVID-19 induces extensive metabolic remodeling in RBCs, disrupting pathways involved in energy metabolism, redox balance, amino acid transport, and oxygen delivery. We identified pronounced sex-dependent metabolic patterns, supporting a more adaptive metabolism for female patients.
RELATED PROJECTS 🔗
Biocardiometab
AIM 🎯
To predict and optimize the pharmacokinetic behavior of therapeutic peptides, addressing key limitations .
METHODOLOGY ⚙️
PeptiKinetics is based on QSAR models specifically developed for peptides using curated experimental datasets and dedicated peptide descriptors. The platform integrates mathematical models and machine-learning approaches to predict peptide-relevant pharmacokinetic endpoints, including aqueous solubility, plasma half-life, blood–brain barrier penetration, hemolytic activity, and Caco-2 permeability.
OUTCOME 📈
Supports the design and prioritization of peptide candidates with improved pharmacokinetic profiles, enabling early identification of stability and delivery liabilities and facilitating the development of more effective and patient-friendly peptide therapeutics.
RELATED PROJECTS 🔗
PeptiMOL, COMPPI…