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Daily Signal March 02, 2026 · 9 min read

Issue #58: Hybrid Computational Framework Integrating Ensemble Learning, Molecular Docking, and Dynamics for Predicting Antimalarial Efficacy of Malaria Box Compounds.

Protein Design Digest - 2026-03-02 - Development of DHODH inhibitors incorporating virtual screening, pharmacophore modeling, fragment-based optimization methods, ADMET, molecular docking, molecular dynamics, PCA analysis, and free energy landscape.

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Hybrid Computational Framework Integrating Ensemble Learning, Molecular Docking, and Dynamics for Predicting Antimalarial Efficacy of Malaria Box Compounds.

The emergence of drug-resistant strains of Plasmodium falciparum continues to challenge global malaria control efforts, underscoring the urgent need for novel therapeutic strategies. In this study, we present an integrative computational framework that combines ensemble machine learning, molecular docking, and molecular dynamics simulations to predict and characterize the antimalarial activity of compounds from the Malaria Box database. Initially, topographical and quantum mechanical descriptors were used to construct regression models for predicting pEC 50 values, but due to the limited predictive performance in the global regression, a classification strategy was adopted, categorizing compounds into “active” and “very active” classes. The best ensemble classifier achieved robust performance (Acc 10 -fold = 0.738, Acc ext = 0.675), with good sensitivity and specificity over individual models. Subsequent regression modeling within each class yielded high predictive accuracy, with ensemble models reaching Q 2 10-fold values of 0.810 and 0.793 for the very active and active classes, respectively. To explore potential mechanisms of action, molecular docking was performed against P. falciparum Cytochrome B, revealing strong binding affinities for most compounds, particularly those forming π-π stacking and hydrogen bonds with Glu272. Molecular dynamics simulations over 200 ns confirmed the stability of several ligand-protein complexes, including unexpected behavior from compound M31, which demonstrated stable binding despite poor docking scores, suggesting a possible competitive inhibition mechanism. Binding free energy calculations further validated these findings, highlighting several promising candidates for future experimental evaluation. This integrative approach offers a powerful platform for accelerating antimalarial drug discovery by combining predictive modeling with mechanistic insights.

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A methodology for accurate benchmarking of neural network accelerators using a high-level synthesis-based hardware generator.

As neural network models continue to grow in scale and complexity, specialized hardware accelerators have emerged to meet the increased demand for compute and memory. These accelerators employ a wide range of architectural innovations, making it challenging to perform fair comparisons and isolate the impact of specific design decisions. Traditional evaluation metrics, such as tera operations per second (TOPS) and TOPS per watt (TOPS/W), are heavily influenced by external factors such as technology node, clock frequency, the scale of the design and workload variations, limiting their effectiveness for meaningful analysis. In this work, we propose a methodology for benchmarking neural network accelerators using Voyager, a high-level synthesis (HLS)-based accelerator generator. Voyager enables the creation of baseline accelerators matched in compute scale and technology node capable of running identical workloads. This enables fair, apples-to-apples comparisons across diverse accelerator architectures. We showcase this methodology with a range of case studies, including those on technology scaling and involving comparisons with state-of-the-art digital accelerators, in-memory computing-based accelerators, and sparsity-aware designs. Our results demonstrate that Voyager-generated designs serve as well-optimized baselines that enable systematic evaluation of accelerator architectures. This article is part of the discussion meeting issue ‘Bits, neurons and qubits for sustainable AI’.

Investigation of the potential mechanism by which methylparaben induces psoriasis: an integrated study using network toxicology, molecular docking, molecular dynamics simulation, and eight machine learning algorithms.

Psoriasis is a chronic inflammatory skin disease with limited safe and effective treatments. Methylparaben, a widely used preservative in cosmetics, pharmaceuticals, and food, is an emerging environmental pollutant linked to immune-related skin disorders, but its role and mechanism in psoriasis remain unclear. This study explored its potential mechanism using network toxicology, molecular docking, molecular dynamics simulation, and eight machine learning algorithms. Methylparaben targets were retrieved from GeneCards and TCMSP, and psoriasis-related targets from CTD and GeneCards. Overlapping targets were screened with Venny 2.1.0. A PPI network was constructed via STRING, and core targets identified using Cytoscape 3.10.2. GO and KEGG enrichment analyses were performed on DAVID. Molecular docking evaluated the binding affinity of methylparaben with key targets. A total of 138 compound-related and 5,592 psoriasis-related targets were identified. Core targets such as INS, HIF1A, and PPARG are involved in regulating immune-inflammatory responses, keratinocyte proliferation and differentiation, and oxidative stress. GO analysis revealed enrichment in xenobiotic metabolism, lipopolysaccharide response, and metal ion binding. KEGG analysis highlighted pathways related to cancer, chemical carcinogenesis from reactive oxygen species, and drug metabolism via cytochrome P450 enzymes. Molecular docking showed stable binding of methylparaben to INS (-4.5 kcal/mol), HIF1A (-5.9 kcal/mol), and PPARG (-5.5 kcal/mol), primarily through hydrogen bonds and hydrophobic interactions. Methylparaben may exert its effects on psoriasis via multi-target and multi-pathway mechanisms, influencing inflammation, oxidative stress, and cellular regulation. These findings provide valuable insight into its toxicological mechanism and potential therapeutic application.

Validating the potential mechanism and therapeutic effect of Qinlian Jiangxia decoction in the treatment of type 2 diabetes mellitus complicated with hyperlipidemia through network pharmacology, molecular docking, molecular dynamics simulation, andexperiments.

Objective To investigate the mechanism of action of Qinlian Jiangxia decoction (, QLJXD) in the treatment of type 2 diabetes mellitus (T2DM) complicated by hyperlipidemia using network pharmacology, molecular docking, molecular dynamics simulation and in vivo experiments. Methods Drug components, targets and disease targets were identified using databases such as TCM systems pharmacology database and analysis platform and GeneCards. The intersecting targets were subjected to protein-protein interaction analysis using the search tool for the retrieval of interacting genes/proteins database. Subsequently, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis of the intersecting targets were conducted using the Metascape platform to identify core components and targets. The results were validated using molecular docking, molecular dynamics simulations and in vivo experiments. Results QLJXD contains 76 active ingredients and 136 disease targets. The core ingredients are quercetin, β-sitosterol, wogonin and baicalein, while the core targets are fatty acid binding protein 4 (FABP4) and peroxisome proliferative activated receptor gamma (PPARG). Molecular docking and molecular dynamics simulations revealed that the core ingredients bound well to the core targets. Animal experiments demonstrated that QLJXD effectively inhibited the expression of FABP4 and increased the expression of PPARG, thereby enhancing disorders of glycolipid metabolism. Conclusion The putative therapeutic efficacy of QLJXD in the management of T2DM complicated with hyperlipidemia may be ascribed to the synergistic actions of multiple components, such as quercetin, β-sitosterol, wogonin, and baicalein, which collectively modulate FABP4 and PPARG molecular targets.

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Unveiling the Small Molecules Binding Site of CD36 Cell Surface Receptor Through Docking and Molecular Dynamics Simulations.

CD36 is a transmembrane glycoprotein involved in lipid uptake and signal transduction, playing a crucial role in various physiological and pathological processes. Read more →

Hybrid Computational Framework Integrating Ensemble Learning, Molecular Docking, and Dynamics for Predicting Antimalarial Efficacy of Malaria Box Compounds.

The emergence of drug-resistant strains of Plasmodium falciparum continues to challenge global malaria control efforts, underscoring the urgent need for novel therapeutic strategies. Read more →

Identification of Novel Extracellular-Signal-Regulated Kinase 2 Inhibitors Through Machine Learning-Driven De Novo Design, Molecular Docking, and Free-Energy Perturbation.

Background : The extracellular-signal-regulated kinase (ERK) cascade regulates cell proliferation, differentiation, and survival, and ERK2 mediates substrate phosphorylation, influencing gene expression and cellular functions. Read more →

Potential inhibitors of Dipeptidyl Peptidase IV dependent from Moroccan phytocompound: molecular docking, molecular dynamics simulations, and MM-PBSA analyses

Type 2 Diabetes Mellitus (T2DM) is the most prevalent form of diabetes and is characterized by beta-cell dysfunction and reduced insulin sensitivity. Read more →

Computational insights into Ru(II)-coumarin complexes as potential anticancer agents: a DFT, QTAIM, NCI-RDG, molecular docking and molecular dynamics approach.

Ru(II) complexes have been explored as promising candidates for novel anticancer agents, due to their significant bioactivity, selective cytotoxicity, and ability to induce apoptosis via multiple signalling pathways, with coumarin derivatives serving as effective ligands to enhance their therapeutic efficacy. Read more →

Evaluation of the mechanism underlying melatonin action in cholestatic liver disease treatment via network pharmacology, molecular docking, and in vivo experiments.

The aim of this study was to investigate the mechanism underlying the action of melatonin (MT) in treating cholestatic liver disease. Read more →

In Silico discovery of GES-5 inhibitors via molecular docking, molecular dynamics simulation studies, and ADMET prediction.

The emergence of multidrug-resistant Enterobacteriaceae represents a major threat to global public health. Read more →

In Silico Identification of Antihypertensive Phytoconstituents in Terminalia arjuna via Molecular Docking, MD Simulation, and DFT Analysis.

Hypertension is a major global health concern, and the exploration of natural compounds as potential antihypertensive agents has been a recent area of study. Read more →

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