TY  - JOUR
T1  - Artificial Intelligence-assisted Drug Development
AU  - Topcu, Irmak Şevval
AU  - Avşar, Orçun
PY  - 2024
DA  - July
Y2  - 2024
JF  - Hitit Journal Of Science
JO  - HJS
PB  - Hitit University
WT  - DergiPark
SN  - 3061-9629
SP  - 8
EP  - 21
VL  - 1
IS  - 1
LA  - en
AB  - Deep learning and machine learning algorithms, two types of artificial intelligence, have come to light as potential solutions to issues and roadblocks in the drug design and discovery process. Both in vitro and in silico techniques have the potential to significantly lower drug development costs when compared to conventional animal models. Early on in the drug research and development process, drug candidates with relevant therapeutic activities can be identified, unsuitable compounds with unwanted side effects can be excluded, and in vitro and in silico techniques can be used to limit the number of drug poisonings. Drug discovery procedures, illness modeling, target identification, artificial intelligence, drug screening, and molecular design can all be completed far more quickly and affordably than with conventional techniques.
KW  - Artificial Intelligence
KW  - In silico approaches
KW  - Target identification
KW  - Target verification
KW  - Target interactions.
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UR  - https://dergipark.org.tr/en/pub/hjs/issue//1501744
L1  - https://dergipark.org.tr/en/download/article-file/4008009
ER  -