Computer-aided drug discovery and development (CADDD): in silico-chemico-biological approach.

By I. Kapetanovic. 2008

553 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Pharmacology, Physical and Theoretical Chemistry, Computational Chemistry & Drug Discovery

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Glaucius Oliva
2. L. Ferreira
3. Ricardo N Dos Santos
4. Maria Batool
5. Alisson Marques da Silva
6. Hojung Nam
7. Tiago Alves de Oliveira
8. Adriano Defini Andricopulo

In Pharmacology

1. Maria Batool
2. Adriano Defini Andricopulo
3. Sangdun Choi
4. Bilal Ahmad

In Physical and Theoretical Chemistry

1. Glaucius Oliva
2. Adriano Defini Andricopulo
3. L. Ferreira
4. Ricardo N Dos Santos

In Computational Chemistry & Drug Discovery

1. Glaucius Oliva
2. Maria Batool
3. Alisson Marques da Silva
4. Hojung Nam
5. Tiago Alves de Oliveira
6. Adriano Defini Andricopulo
7. Letícia C. Assis
8. L. Ferreira

Citations in the corpus

• Molecular Docking and Structure-Based Drug Design Strategies (2015, background): “The identification of the most likely binding conformations requires two steps: (i) exploration of a large conformational space representing various potential binding modes; (ii) accurate prediction of the interaction energy associated with each of the predicted binding conformations [24].”
• A Structure-Based Drug Discovery Paradigm (2019, background): “The appropriate ligand-binding conformation depends on two factors: (i) large conformational space defining possible binding poses and (ii) explicit prediction of binding energy correlating with each conformation [51].”
• Structure-Based Virtual Screening: From Classical to Artificial Intelligence (2020, background): “Kapetanovic (2008) briefly notes that CADD comprises (i) making the drug discovery and development process faster with the contribution of in silico simulations; (ii) optimizing and identifying new drugs using the computational approach to discover chemical and biological information about possible…”
• DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences (2018, background): “However, the identification of DTIs in large-scale chemical or biological experiments usually takes 2~3 years of experiments, with high associated costs [1].”
• Closing the loop between brain and electrical stimulation: towards precision neuromodulation treatments (2023, background): “In-silico studies, including machine learning, computational modeling, and simulation approaches, have also provided new mechanistic insights to assess the ef fi cacy of interventions to modulate a brain-based target [7 – 9].”