⌘+K

Pulse Explore Communities Researchers Journals

Home Communities Explore

March 26, 2026Protein and Peptide Letters

Corrigendum to: Reviewing the Context of Molecular Modeling to Enhance the Application of Machine Learning Technologies for Safer Bioinformatics

Márcio V. Ramos

View at Publisher

Ask AI

Bookmark

Share

Key Points

To correct minor production errors identified in the previously published article related to molecular modeling and machine learning.
Correction of an incorrect link under Heading 4.
Clarification of interaction energy calculations in molecular docking.
Confirmation that only grid points are utilized for computational efficiency.
Safe computational practices for interaction energy calculations are maintained.
The corrigendum ensures accurate referencing and clarity in methods as detailed in the original article.

Implication

This corrigendum clarifies errors in methods for calculating interaction energy in bioinformatics, highlighting corrections.

Abstract

No abstract yet. The journal hasn't published one we can show.

Discussion

No takes yet. Share an insight, caveat, or question.

Cite This Study

Márcio V. Ramos (2026) studied this question.

synapsesocial.com/papers/69ccb7c216edfba7beb89df1 https://doi.org/10.2174/0929866533999260326091022

View at Publisher

Ask AI

Bookmark

Share

Keep reading

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1Innovations in Molecular Docking: A Detailed Analysis of Methodological Developments and Their Applications in Drug Discovery2024
2Key Topics in Molecular Docking for Drug DesignReview · 2019 · 645 citations
3Classical Docking to Machine Learning Based Docking: MolecularDocking in Drug DiscoveryReview · 2026 · 3 citations
4Prediction of Protein−Ligand Interactions. Docking and Scoring: Successes and GapsReview · 2006 · 647 citations
5Uni-Mol Docking V2: Towards Realistic and Accurate Binding Pose Prediction2024 · 21 citations

Innovative Integration of Molecular Docking and Machine Learning for Drug Discovery: From Virtual Screening to Nanomolar Inhibitors2026
Modern <scp>machine‐learning</scp> for binding affinity estimation of <scp>protein–ligand</scp> complexes: Progress, opportunities, and challenges2024
Editorial: Dynamics and functional exploration of pharmacologically active proteins2026
Heat shock proteins as modulators of cardiovascular health and disease: From cellular stress to clinical targetsProtein and Peptide Letters · 2026
Advancing Antimicrobial Peptides: Mechanisms, Design, and Applications in the Post-Antibiotic EraProtein and Peptide Letters · 2026

PMore from Protein and Peptide Letters

More from Universidade Federal do Ceará