# Artificial Intelligence in Oncology
## 3 AI in New Drug Synthesis and Clinical Trials  
The development of a given drug is a complicated process. One of the ways to improve this process is to study the reuse of previously used drugs. The traditional method of creating drugs is based on finding targets that are the starting point for further research into their relationship with a given disease. The second approach is the synthesis of new drugs. The use of new technologies, including AI, to create new drugs is not only a result of the development of new technologies, but has even become a necessity when the traditional synthesis of new drugs has proven to be time-consuming and costly. Four directions can be distinguished in the use of AI in the development of new cancer drugs: assessment of the properties of the molecule under investigation, prediction of its activity, its synthesis from scratch, and drug-receptor interactions and prediction of drug response [54]. In the construction of new oncological drugs, different models developed by AI are used, which take into account such parameters as tumour growth kinetics, molecular profiling and pharmacological properties [55]. This is done by detecting new molecules, e.g., proteins and nucleic acids important in the process of tumour growth, making it possible to design new drugs oriented towards the above-mentioned molecules, e.g., tracking the interaction between KRAS proteins and cell membranes in more detail than before. AI's task is to analyse biological data to identify potential targets, such as proteins or genes, important in the process of tumour growth related to a given disease [56]. AI also has the ability to analyse patient data and predict potential treatment outcomes and potential toxicity. Potential targets in AI research on the synthesis of new cancer drugs include the STK 33 pathway (serine/threonine kinase). STK 33 is involved in the process of cancer initiation, progression and resistance to treatment. [PERSON] et al. described the use of AI in the creation of potential EGFR inhibitors for breast cancer therapy [57]. Using the prediction model developed, they applied the technique of hybridizing the N-substituted quinazolin-4-amine scaffold with EGFR inhibitors, resulting in the synthesis of 18 new molecules, of which, after further selection, one had the highest activity against EGFR [57]. Although AI is accelerating the development of new drug research, it comes with certain limitations, well described by [PERSON] et al. [58]. The role of AI in clinical trials includes clinical trial design, patient enrichment, recruitment and enrolment, investigator and site selection, patient monitoring, medication adherence and retention. The way clinical trials have been conducted to date can be described as linear and sequential. Unfortunately, this has disadvantages, such as suboptimal patient selection and difficulties in effectively managing and monitoring patients, resulting in prolonged trial duration and leading to high failure rates and high costs [59]. This traditional approach is lengthy, with only a 10% success rate [59]. AI plays a special role in clinical trials. When constructing new oncological drugs, attention is paid to the variability of the genome of cancer cells and the potential activity of new drugs. This can be achieved by constructing various AI models that predict drug activity and disease prognosis depending on the state of gene mutations in various cancers [60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73]. AI enables patient recruitment by combining diverse demographic and medical patient data to match the requirements of a given research protocol by using methods such as population heterogeneity reduction and prognostic and predictive enrichment [74]. Reduction of population heterogeneity implies selection of patients with baseline measurements of a disease or a biomarker characterising the disease in a narrow range, while excluding patients whose disease or symptoms improve spontaneously or whose measurements are highly variable, in order to increase study power.