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: Briefings in Bioinformatics

Article Title: Comprehensive classification of TP53 somatic missense variants based on their impact on p53 structural stability

doi: 10.1093/bib/bbae400

Figure Lengend Snippet: Ramachandran density plot of TP53 deleterious somatic missense variants. (A) RDPs for deleterious variants (p.A159D, p.Q192L, p.N210K, and p.T231N). The colours from blue to red represent low to high density. (B) The differences between the variants and the based files. The colours from blue to red represent from diminished to increased density.

Article Snippet: We previously developed a method named Ramachandran Plot–Molecular Dynamics Simulations (RP-MDS), aiming to predict the function of germline missense variants based on their effects on protein structure stability, and successfully applied to predict the deleteriousness of unclassified germline missense variants in multiple cancer genes.

Techniques:

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: