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Daily Signal April 06, 2026 · 8 min read

Issue #83: Quantum chemical, spectroscopic, molecular docking, molecular dynamics analyses and ADMET properties: Nifedipine.

Protein Design Digest #83: BA-Pred and RMSD-Pred: Integrated Graph Neural Network Models for Accura…

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Signal of the Day

Quantum chemical, spectroscopic, molecular docking, molecular dynamics analyses and ADMET properties: Nifedipine.

Nifedipine is known as a calcium channel blocker and is used in hypertension, antianginal medication, and Raynaud syndrome. Nifedipine, which blocks voltage-dependent L-type calcium channels in the smooth muscle cells of the vessels and reduces intracellular calcium concentration, provides muscle relaxation and vasodilation. Nifedipine is used to reduce spasms in the hands and feet in Raynaud syndrome, improving blood circulation and reducing symptoms. In this study, molecular structure analyses of the nifedipine molecule began with an optimization study using the DFT/B3LYP method and continued with frequency, HOMO-LUMO, MEP, and hyperpolarizability analyses. Potential energy distribution is also presented with experimental FTIR-ATR and FT-Raman spectra. Molecular docking and molecular dynamics (MD) studies aimed to elucidate the mechanism by which nifedipine blocks voltage-dependent L-type calcium channels. Nifedipine binding stability was analyzed for 100 ns simulation time in Nifedipine-Protein Complex (Holo), protein (Apo), and Holo (POPC) system with lipid membrane media using molecular dynamics (MD) analysis. Finally, the ADMET profile of nifedipine was determined, and information on its pharmacokinetic properties was presented. This study, which includes molecular-level analyses, is a reference scientific study for drug candidates that are expected to be developed for the treatment of various diseases, such as hypertension and Raynaud syndrome.

Why this matters:

Also Worth Reading

Enhancing CYP450-Ligand Binding Predictions: A Comparative Analysis of Ligand-Based and Hybrid Machine Learning Models.

Predicting cytochrome P450 (CYP450) ligand binding is critical in early-stage drug discovery as CYP450-mediated metabolism profoundly influences drug efficacy, safety, and adverse reaction risks. However, experimental determination of CYP450-ligand interactions remains resource- and time-intensive, underscoring the need for robust computational alternatives. While ligand-based methods are commonly employed, they often fail to fully account for structural intricacies governing protein-ligand interactions. To address this gap, we developed a hybrid machine learning framework integrating ligand descriptors, protein descriptors, and protein-ligand interaction descriptors that include molecular docking-derived parameters, rescoring function components from multiple algorithms, and structural interaction fingerprints (SIFt). Evaluated on CYP1A2 and CYP17A1 isoforms, our model demonstrated superior predictive accuracy in cross-validation compared with stand-alone molecular docking and ligand-based approaches. Furthermore, benchmarking against state-of-the-art tools (SwissADME and ADMETlab 3.0) revealed enhanced performance in binding prediction. This work establishes a versatile framework for advancing computational tools to prioritize CYP450 binding assessments during drug discovery.

Evaluating zero-shot prediction of monomeric protein design success by AlphaFold, ESMFold, and ProteinMPNN.

De novo protein design has enabled the creation of proteins with diverse functionalities that are not found in nature. Despite recent advances, experimental success rates remain inconsistent and context-dependent, posing a bottleneck for broader applications of de novo design. To overcome this, structure and sequence prediction models have been applied to assess design quality prior to experimental testing to save time and resources. In this study, we examined the extent to which AlphaFold, Protein MPNN, and ESMFold can discriminate between experimentally successful and unsuccessful designs. We first curated a benchmark dataset of 614 experimentally characterized de novo designed monomers from 11 different design studies between 2012 and 2021. All predictive models demonstrated moderate ability to discriminate experimental successes (expressed, soluble, monomeric, and fold with the correct secondary structure) from failures. Still, many failed designs have better confidence metrics than successful designs, and confidence metrics were topology-dependent. Among all computational models evaluated, ESMFold average predicted local-distance difference test (pLDDT) yielded the best individual performance at distinguishing between successful and unsuccessful designs. A logistic regression model combining all confidence metrics provided only modest improvement over ESMFold pLDDT alone. Overall, these results show that these models can serve as an initial filtering strategy prior to experimental validation; however, their utility at accurately predicting experimentally successful designs remains limited without task-specific training.

Molecular docking, MM/GBSA, FEP/MD, and DFT/MM MD studies on predicting binding affinity of carbonic anhydrase II inhibitors.

Human carbonic anhydrase II (hCAII) is one of the zinc-containing metalloenzymes that catalyzes various hydration reactions. We report an investigation of whether modern computational tools can predict inhibitory potency of a set of sulfonamides against hCAII. The methods used are molecular docking, molecular dynamics simulations coupled with the free energy perturbation theory (FEP/MD), molecular mechanics combined with the generalized Born and surface area continuum solvation (MM/GBSA), and quantum mechanics/molecular mechanics (QM/MM) metadynamics simulations. A comparison is presented between experimental and computed binding free energy properties. All MD-based approaches demonstrate robust performance with R 2 values in the range of 0.89 to 0.99 for the subset of structurally simple sulfonamides, underscoring the importance of accounting for dynamic protein-ligand interactions. With regard to the other subset comprised of structurally more diverse sulfonamides, for which the QM(B3LYP)/MM(CHARMM) metadynamics approach is less affordable, the FEP/MD method yields an R 2 value of 0.70. Notably, the R 2 value increases to 0.80 after the removal of one outlier (chlorzolamide).

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Molecular Docking and Molecular Dynamics Simulation Study of Creatinine Interaction with Human Protein 7XTQ

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Quantum chemical, spectroscopic, molecular docking, molecular dynamics analyses and ADMET properties: Nifedipine.

Nifedipine is known as a calcium channel blocker and is used in hypertension, antianginal medication, and Raynaud syndrome. Read more →

Discovery of WRN helicase inhibitors by 3D-CNN docking and ML consensus from traditional Chinese medicine monomers.

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Pipeline Tip

Always validate pLDDT scores before using AlphaFold models for docking.

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The protein structure is the language of life; design is its poetry. — Recep Adiyaman

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