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Daily Signal February 26, 2026 · 7 min read

Issue #56: A Neural Time-Series Learning Method for Accelerating Free-Energy Perturbation and Rare-Event Molecular Dynamics Simulations.

Protein Design Digest - 2026-02-26 - Discovery of potent ALK tyrosine kinase inhibitors for thyroid cancer via machine learning modeling, molecular docking, MD simulations, and DFT study.

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A Neural Time-Series Learning Method for Accelerating Free-Energy Perturbation and Rare-Event Molecular Dynamics Simulations.

Molecular dynamics (MD) simulations are central to materials and drug discovery yet remain computationally demanding, particularly for free-energy perturbation (FEP) protocols and rare-event sampling. Existing sequence-based accelerators, especially Long Short-Term Memory (LSTM) models, often fail to capture long-range temporal structure and provide sufficient expressive capacity in noisy trajectories. Here, we introduce BiLSTMK-MD, a neural time-series learning method that constructs a causality-preserving surrogate for MD and FEP trajectories to reduce sampling requirements. The approach couples a sliding-window bidirectional LSTM encoder, which preserves long-range correlations, with an attention mechanism to enhance temporally informative frames, while a Kolmogorov-Arnold network output layer applies expressive nonlinear readout to decode the attention-refined representation into the final output. A two-stage, fANOVA-guided Bayesian optimization process searches for the optimal hyperparameter configuration for each system. Across four data sets, BiLSTMK-MD achieves mean absolute errors below 1.5 kcal mol-1 for window-resolved free-energy increments, reconstructs dihedral free-energy basins from 1-10% of trajectories, and maintains performance across systems. In long-trajectory regimes, it affords up to 400-fold acceleration for FEP and >700-fold speedup for rare-conformation sampling over conventional MD/FEP simulation. This neural time-series surrogate therefore provides a route to reducing sampling demands for free-energy estimation and rare-event characterization.

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Also Worth Reading

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.

scDock: Streamlining drug discovery targeting cell-cell communication via scRNA-seq analysis and molecular docking

Summary Identifying drugs that target intercellular communication networks represents a promising therapeutic strategy, yet linking single-cell RNA sequencing (scRNA-seq) analysis to structure-based drug screening remains technically challenging and requires substantial bioinformatics expertise. We present scDock, an integrated and user-friendly pipeline that seamlessly connects scRNA-seq data processing, cell–cell communication inference, and molecular docking-based drug discovery. Through a single configuration file, users can execute the complete workflow, from raw scRNA-seq data to ranked drug candidates, without programming skills. scDock automates the identification of disease-relevant ligand–receptor interactions from scRNA-seq data and perfoms structure-based virtual screening against these communication targets using Protein Data Bank (PDB) or AlphaFold-predicted protein structures. The pipeline generates comprehensive outputs at each stage, enabling users to explore intercellular signaling alterations and discover therapeutic compounds targeting specific cell–cell communications. scDock addresses a critical gap by providing an accessible end-to-end solution for communication-targeted drug discovery from single-cell data. Availability and Implementation scDock is freely available at https://github.com/Andrewneteye4343/scDock . It is implemented in R, Python, shell scripts, and supports Linux systems, including Ubuntu and Debian.

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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A Neural Time-Series Learning Method for Accelerating Free-Energy Perturbation and Rare-Event Molecular Dynamics Simulations.

Molecular dynamics (MD) simulations are central to materials and drug discovery yet remain computationally demanding, particularly for free-energy perturbation (FEP) protocols and rare-event sampling. Read more →

Pipeline Tip

Normalise thermal B-factors when comparing different crystal structures.

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Deep learning is not a magic wand, but a powerful lens for structural biology. — Recep Adiyaman

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