Editing Computational biology (section)

=== Drug Discovery ===
Computational biology has become instrumental in revolutionizing [[drug discovery]] processes. By integrating computational systems biology approaches, researchers can model complex biological systems, facilitating the identification of novel drug targets and the prediction of drug responses. These methodologies enable the simulation of [[intracellular]] and [[intercellular signaling]] events using data from genomic, proteomic, or metabolomic experiments, thereby streamlining the drug development pipeline and reducing associated costs.<ref>{{Cite journal |last1=Materi |first1=Wayne |last2=Wishart |first2=David S. |date=April 2007 |title=Computational systems biology in drug discovery and development: methods and applications |url=https://linkinghub.elsevier.com/retrieve/pii/S1359644607000943 |journal=Drug Discovery Today |language=en |volume=12 |issue=7–8 |pages=295–303 |doi=10.1016/j.drudis.2007.02.013|pmid=17395089 }}</ref>

Moreover, the convergence of computational biology with artificial intelligence (AI) has further accelerated drug design. AI-driven models can analyze vast datasets to predict molecular behavior, optimize lead compounds, and anticipate potential side effects, thereby enhancing the efficiency and effectiveness of drug discovery.<ref>{{Cite journal |last1=Zhang |first1=Yue |last2=Luo |first2=Mengqi |last3=Wu |first3=Peng |last4=Wu |first4=Song |last5=Lee |first5=Tzong-Yi |last6=Bai |first6=Chen |date=November 2022 |title=Application of Computational Biology and Artificial Intelligence in Drug Design |journal=International Journal of Molecular Sciences |language=en |volume=23 |issue=21 |pages=13568 |doi=10.3390/ijms232113568 |doi-access=free |issn=1422-0067 |pmc=9658956 |pmid=36362355}}</ref>