Many experimental techniques in geophysics advance the understanding of Earth processes by estimating and interpreting Earth structure (e.g. velocity and/or density structure). Different types of geophysical data can be collected and analysed separately, sometimes resulting in inconsistent models of the Earth depending on the data used. We present a constrained optimization approach for a joint inversion least-squares (LSQ) algorithm to characterize 1-D Earth's structure. We use two geophysical data sets sensitive to shear velocities: receiver function and surface wave dispersion velocity observations. We study the use of bound constraints on the regularized inverse problem, which are more physical than the regularization parameters required by conventional unconstrained formulations. Specifically, we develop a constrained optimization formulation that is solved with a primal-dual interior-point (PDIP) method, and validate our results with a traditional, unconstrained formulation that is solved with a truncated singular value decomposition (TSVD) for a set of numerical experiments with synthetic crustal velocity models. We conclude that the PDIP results are as accurate as those from the regularized TSVD approach, are less affected by noise, and honour the geophysical constraints.

Background/Objectives: Colombia harbors exceptional plant diversity, comprising over 31,000 formally identified species, of which approximately 6000 are classified as useful plants. Among these, 2567 species possess documented food and medicinal applications, with several traditionally utilized for managing febrile illnesses. Despite the global burden of dengue virus infection affecting millions annually, no specific antiviral therapy has been established. This study aimed to identify potential anti-dengue compounds from Colombian medicinal flora through machine learning-based quantitative structure–activity relationship (QSAR) modeling. Methods: An optimized XGBoost algorithm was developed through Bayesian hyperparameter optimization (Optuna, 50 trials) and trained on 2034 ChEMBL-derived activity records with experimentally validated anti-dengue activity (IC₅₀/EC₅₀). The model incorporated 887 molecular features comprising 43 physicochemical descriptors and 844 ECFP4 fingerprint bits selected via variance-based filtering. IC₅₀ and EC₅₀ endpoints were modeled independently based on their pharmacological distinction and negligible correlation (r = −0.04, p = 0.77). Through a systematic literature review, 2567 Colombian plant species from the Humboldt Institute’s official checklist were evaluated (2501 after removing duplicates and infraspecific taxa), identifying 358 with documented antiviral properties. Phytochemical analysis of 184 characterized species yielded 3267 unique compounds for virtual screening. A dual-endpoint classification strategy categorized compounds into nine activity classes based on combined potency thresholds (Low: pActivity ≤ 5.0, Medium: 5.0 < pActivity ≤ 6.0, High: pActivity > 6.0). Results: The optimized model achieved robust performance (Matthews correlation coefficient: 0.583; ROC-AUC: 0.896), validated through hold-out testing (MCC: 0.576) and Y-randomization (p < 0.01). Virtual screening identified 276 compounds (8.4%) with high predicted potency for both endpoints (“High-High”). Structural novelty analysis revealed that all 276 compounds exhibited Tanimoto similarity < 0.5 to the training set (median: 0.214), representing 145 unique Murcko scaffolds of which 144 (99.3%) were absent from the training data. Application of drug-likeness filtering (QED ≥ 0.5) and applicability domain assessment identified 15 priority candidates. In silico ADMET profiling revealed favorable pharmaceutical properties, with Incartine (pIC₅₀: 6.84, pEC₅₀: 6.13, QED: 0.83), Bilobalide (pIC₅₀: 6.78, pEC₅₀: 6.07, QED: 0.56), and Indican (pIC₅₀: 6.73, pEC₅₀: 6.11, QED: 0.51) exhibiting the highest predicted potencies. Conclusions: This systematic computational screening of Colombian medicinal flora demonstrates the untapped potential of regional biodiversity for anti-dengue drug discovery. The identified candidates, representing structurally novel chemotypes, are prioritized for experimental validation.

Background/Objectives: Colombia harbors exceptional plant diversity, comprising over 31,000 formally identified species, of which approximately 6,000 are classified as useful plants. Among these, 2,567 species possess documented food and medicinal applications, with several traditionally utilized for managing febrile illnesses. Despite the global burden of dengue virus infection affecting millions annually, no specific antiviral therapy has been established. This study aimed to identify potential anti-dengue compounds from Colombian medicinal flora through machine learning-based quantitative struc-ture-activity relationship (QSAR) modeling. Methods: An optimized XGBoost algorithm was implemented through Bayesian hyperparameter optimization (Optuna, 50 trials) to develop a QSAR model trained on 2,034 ChEMBL-derived activity records with experi-mentally validated anti-dengue activity (IC₅₀/EC₅₀). The model incorporated 887 molecular descriptors, comprising 43 physicochemical properties and 844 ECFP4 fingerprint bits, selected via variance-based feature selection. Bayesian hyperparameter optimization us-ing Optuna (50 trials) was performed to maximize model performance. Through system-atic literature review, 2,567 Colombian plant species were evaluated, identifying 358 with documented antiviral properties. Phytochemical analysis of 184 species generated 3,267 unique compounds for subsequent virtual screening. Compounds were prioritized based on predicted activity, drug-likeness, and applicability domain assessment for future ex-perimental validation. A dual-endpoint classification strategy was employed to simulta-neously evaluate both IC50 and EC50 activities, with compounds categorized into nine activity classes based on combined potency thresholds (Low: pActivity ≤ 5.0, Medium: 5.0 < pActivity ≤ 6.0, High: pActivity > 6.0). Results: The optimized XGBoost model achieved robust performance with a Matthews correlation coefficient of 0.583 and area under the receiver operating characteristic curve of 0.896. Virtual screening of 3,267 Colombian phytochemicals identified 276 compounds (8.4%) with high predicted potency (pActivity > 6) for both IC₅₀ and EC₅₀ endpoints (classified as "High-High"). Comprehensive struc-ture-activity relationship (SAR) analysis revealed that 239 of these compounds (86.6%) represented structurally novel chemotypes with low similarity (Tanimoto < 0.5) to the training dataset. Application of drug-likeness filters (QED ≥ 0.5) identified 20 priority can-didates (7.2% of high-potency hits), with 12 compounds showing exceptional profiles. In-cartine (pIC50: 6.84, pEC50: 6.13, QED: 0.83), Bilobalide (pIC50: 6.78, pEC50: 6.07, QED: 0.56), and Indican (pIC50: 6.73, pEC50: 6.11, QED: 0.51) exhibited the highest predicted potencies. Descriptor-activity correlation analysis identified QED (ρ = 0.14 with EC50), TPSA (ρ = -0.15), and aromatic rings as key modulators of antiviral activity. Conclusions: This pioneering systematic computational screening of Colombian flora for anti-dengue activity demonstrates the untapped potential of regional biodiversity in pharmaceutical discovery. The identified lead compounds represent prioritized candidates for experi-mental validation and subsequent development of dengue therapeutics, with all compu-tational resources made publicly available to facilitate future research.