Journal: Scientific Reports

Article Title: Exploring the nuclear proteins, viral capsid protein, and early antigen protein using immunoinformatic and molecular modeling approaches to design a vaccine candidate against Epstein Barr virus

doi: 10.1038/s41598-024-66828-x

Figure Lengend Snippet: Molecular dynamics simulation plot showing ( A ) RMSF plot for TLR 2-Vaccine, ( B ) RMSF plot for TLR 9-Vaccine, ( C ) Contact map for TLR 2-Vaccine, ( D ) Contact map for TLR 9-Vaccine.

Article Snippet: Figure 6 Molecular dynamics simulation plot showing ( A ) RMSF plot for TLR 2-Vaccine, ( B ) RMSF plot for TLR 9-Vaccine, ( C ) Contact map for TLR 2-Vaccine, ( D ) Contact map for TLR 9-Vaccine.

Techniques:

Journal: Journal of Cheminformatics

Article Title: A beginner’s approach to deep learning applied to VS and MD techniques

doi: 10.1186/s13321-025-00985-7

Figure Lengend Snippet: Overview of the DeepScreening workflow employed by Joshi et al. for the screening of natural compounds against 3CL pro . Through a LBVS step employing a DL predictive model, a SBVS step employing a traditional molecular docking method, additional in silico screenings for characteristics such as pharmacokinetics and toxicity, and MD simulations, a database of 1,611 compounds was narrowed down to two specific hit compounds for further testing

Article Snippet: Tam et al. developed the Deep Learning Ramachandran Plot-Molecular Dynamics Simulations workflow, or DL-RP-MDS, for the functional classification of genetic variants (Fig. ) [ ].

Techniques: In Silico, Drug discovery

Journal: Journal of Cheminformatics

Article Title: A beginner’s approach to deep learning applied to VS and MD techniques

doi: 10.1186/s13321-025-00985-7

Figure Lengend Snippet: Overview of the workflow employed by Arshia et al. for the in silico compound generation of 3CL pro inhibitors. An LSTM RNN architecture was trained through DTL for the generation of 3CL pro binding molecules. Each generation step, the generated molecules were further validated and tested using traditional molecular docking methods. A genetic algorithm then selected a limited number of compounds for further finetuning of the RNN model. After ten generation steps, all molecules with high binding affinity for 3CL pro were clustered through a hierarchical clustering method, and the compounds with the highest binding affinity in each cluster were selected for further testing

Article Snippet: Tam et al. developed the Deep Learning Ramachandran Plot-Molecular Dynamics Simulations workflow, or DL-RP-MDS, for the functional classification of genetic variants (Fig. ) [ ].

Techniques: In Silico, Binding Assay, Generated

Journal: Journal of Cheminformatics

Article Title: A beginner’s approach to deep learning applied to VS and MD techniques

doi: 10.1186/s13321-025-00985-7

Figure Lengend Snippet: Summary of DL models mentioned throughout the “ Deep learning and virtual screening ” section of this review used to aid in performing VS workflows

Article Snippet: Tam et al. developed the Deep Learning Ramachandran Plot-Molecular Dynamics Simulations workflow, or DL-RP-MDS, for the functional classification of genetic variants (Fig. ) [ ].

Techniques: Generated, Binding Assay, Drug discovery, In Silico, In Vitro, In Vivo, Plasmid Preparation, Diffusion-based Assay, Sequencing, Modification, Protein Binding, Sampling, Molecular Weight

Journal: Journal of Cheminformatics

Article Title: A beginner’s approach to deep learning applied to VS and MD techniques

doi: 10.1186/s13321-025-00985-7

Figure Lengend Snippet: Summary of DL models mentioned throughout the “ Deep learning and molecular dynamics simulations ” section of this review used to aid in performing MD workflows

Article Snippet: Tam et al. developed the Deep Learning Ramachandran Plot-Molecular Dynamics Simulations workflow, or DL-RP-MDS, for the functional classification of genetic variants (Fig. ) [ ].

Techniques: Sampling, Residue, Functional Assay

Journal: Lab on a chip

Article Title: Mass-Producible Microporous Silicon Membranes for Specific Leukocyte Subset Isolation, Immunophenotyping, and Personalized Immunomodulatory Drug Screening In Vitro

doi: 10.1039/c9lc00315k

Figure Lengend Snippet: Finite element modeling of the deflection of the SiMIPA microporous membrane under fluid flow. (a) Rendering from COMSOL showing the deflection of a membrane with 20 μm holes under a 2000 μL/sec flow rate. (b) A plot of the simulated deflection of a 3 × 3 × 0.05 mm3 Si membrane under different flow rates while the 3 × 0.05 mm2 faces are fixed. The membrane has a hexagonal lattice of pores through the 0.05 mm thick membrane where the center-to-center spacing is equal 30 μm. The linear regression line intercepts at y = 0 and has R2 = 0.9925. (c) Rendering from COMSOL showing the von Mises stress on a membrane with 20 μm holes under a 2000 μL/sec flow rate. (d) A plot of the simulated maximum von Mises stress observed on a 3 × 3 × 0.05 mm3 membrane under different flow rates while the 3 × 0.05 mm2 faces are fixed. The membrane has a hexagonal lattice of pores through the 0.05 mm thick membrane where the center-to-center spacing is 30 μm. The linear regression line intercepts at y = 0 and has R2 = 0.9925.

Article Snippet: The deflection profile of the entire membrane was reconstructed using the mirroring feature in COMSOL. fig ft0 fig mode=article f1 fig/graphic|fig/alternatives/graphic mode="anchored" m1 Open in a separate window Fig. 3. caption a7 Finite element modeling of the deflection of the SiMIPA microporous membrane under fluid flow. (a) Rendering from COMSOL showing the deflection of a membrane with 20 μm holes under a 2000 μL/sec flow rate. (b) A plot of the simulated deflection of a 3 × 3 × 0.05 mm 3 Si membrane under different flow rates while the 3 × 0.05 mm 2 faces are fixed.

Techniques: Membrane