A Review on Artificial Intelligence Approaches and Rational Approaches in Drug Discovery
Copyright© Bentham Science Publishers; For any queries, please email at epubbenthamscience.net..
Artificial intelligence (AI) speeds up the drug development process and reduces its time, as well as the cost which is of enormous importance in outbreaks such as COVID-19. It uses a set of machine learning algorithms that collects the available data from resources, categorises, processes and develops novel learning methodologies. Virtual screening is a successful application of AI, which is used in screening huge drug-like databases and filtering to a small number of compounds. The brain's thinking of AI is its neural networking which uses techniques such as Convoluted Neural Network (CNN), Recursive Neural Network (RNN) or Generative Adversial Neural Network (GANN). The application ranges from small molecule drug discovery to the development of vaccines. In the present review article, we discussed various techniques of drug design, structure and ligand-based, pharmacokinetics and toxicity prediction using AI. The rapid phase of discovery is the need of the hour and AI is a targeted approach to achieve this.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:29 |
|---|---|
| Enthalten in: |
Current pharmaceutical design - 29(2023), 15 vom: 06. Juni, Seite 1180-1192 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Srivathsa, Anjana Vidya [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Artificial intelligence |
|---|
| Anmerkungen: |
Date Completed 12.06.2023 Date Revised 08.04.2024 published: Print Citation Status MEDLINE |
|---|
| doi: |
10.2174/1381612829666230428110542 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM356354601 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM356354601 | ||
| 003 | DE-627 | ||
| 005 | 20240409232115.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.2174/1381612829666230428110542 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1370.xml |
| 035 | |a (DE-627)NLM356354601 | ||
| 035 | |a (NLM)37132148 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Srivathsa, Anjana Vidya |e verfasserin |4 aut | |
| 245 | 1 | 2 | |a A Review on Artificial Intelligence Approaches and Rational Approaches in Drug Discovery |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 12.06.2023 | ||
| 500 | |a Date Revised 08.04.2024 | ||
| 500 | |a published: Print | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright© Bentham Science Publishers; For any queries, please email at epubbenthamscience.net. | ||
| 520 | |a Artificial intelligence (AI) speeds up the drug development process and reduces its time, as well as the cost which is of enormous importance in outbreaks such as COVID-19. It uses a set of machine learning algorithms that collects the available data from resources, categorises, processes and develops novel learning methodologies. Virtual screening is a successful application of AI, which is used in screening huge drug-like databases and filtering to a small number of compounds. The brain's thinking of AI is its neural networking which uses techniques such as Convoluted Neural Network (CNN), Recursive Neural Network (RNN) or Generative Adversial Neural Network (GANN). The application ranges from small molecule drug discovery to the development of vaccines. In the present review article, we discussed various techniques of drug design, structure and ligand-based, pharmacokinetics and toxicity prediction using AI. The rapid phase of discovery is the need of the hour and AI is a targeted approach to achieve this | ||
| 650 | 4 | |a Review | |
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a drug design | |
| 650 | 4 | |a neural network | |
| 650 | 4 | |a recursive neural network | |
| 650 | 4 | |a toxicity | |
| 650 | 4 | |a virtual screening | |
| 700 | 1 | |a Sadashivappa, Nandini Markuli |e verfasserin |4 aut | |
| 700 | 1 | |a Hegde, Apeksha Krishnamurthy |e verfasserin |4 aut | |
| 700 | 1 | |a Radha, Srimathi |e verfasserin |4 aut | |
| 700 | 1 | |a Mahesh, Agasa Ramu |e verfasserin |4 aut | |
| 700 | 1 | |a Ammunje, Damodar Nayak |e verfasserin |4 aut | |
| 700 | 1 | |a Sen, Debanjan |e verfasserin |4 aut | |
| 700 | 1 | |a Theivendren, Panneerselvam |e verfasserin |4 aut | |
| 700 | 1 | |a Govindaraj, Saravanan |e verfasserin |4 aut | |
| 700 | 1 | |a Kunjiappan, Selvaraj |e verfasserin |4 aut | |
| 700 | 1 | |a Pavadai, Parasuraman |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Current pharmaceutical design |d 1998 |g 29(2023), 15 vom: 06. Juni, Seite 1180-1192 |w (DE-627)NLM095430172 |x 1873-4286 |7 nnns |
| 773 | 1 | 8 | |g volume:29 |g year:2023 |g number:15 |g day:06 |g month:06 |g pages:1180-1192 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.2174/1381612829666230428110542 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 29 |j 2023 |e 15 |b 06 |c 06 |h 1180-1192 | ||