It has always been an important scientific topic to investigate the biological activities of newly synthesized compounds. This is connected to the never-ending interest in drug discovery or in finding a molecule with certain \"useful\" properties that can affect a function or process in the human body. Selecting the most promising targets from the huge pool of diverse potential candidates is one of the major difficulties of the post-genome era [18]. The choice of \"the right\" biological target may be the most crucial one made in pharmacological research and development [19]. This group includes biotherapeutics as well as small molecules. Computational technologies and big data are becoming effective in predicting biological target drug ability and the drug-likeness of new therapeutic agents, as experimental research approaches become less common [20]. The multiple stages that must be completed between the decision to choose a target and the start of clinical trials to prove efficacy in humans typically follow a clear-cut pattern. After screening and hit identification, optimization rounds based on pharmacological and toxicological testing are conducted, and then pharmaceutical development and production take place. There are a number of limitations to the systematic use of experiments in the drug discovery process. A few of these elements are the frequency of the newly synthesized compounds, the quantitative restrictions of tissue samples, and the need to restrict animal testing. In this situation, it is conceivable to presume that in silico computer models, which are both an excellent supplement and a practical replacement for biological investigations, may be used to replace biological investigations [21; 22; 23]. A drug candidate needs to reach its pharmacological target within the body, reach the right concentration at the site of action, and stay there long enough to be utilized as a medicine. Due to their poor pharmacokinetics and bioavailability, many promising biologically active compounds that are intended for use as medications fail. In silico research has made it possible to identify new drugs through target identification and validation, contributing to ongoing advancements in the drug discovery and development process. Several methods are employed to assess possible compounds with drug-like characteristics, including quantitative structure-activity and structure-property relationship models and in silico screening, which calculate anticipated biological effects, solubility, sufficient oral bioavailability, synthetic accessibility, intestinal absorption, and blood-brain barrier penetration [24; 25; 26; 27]. Established through analyses of the physiochemical or structural characteristics of small-scale organic drugs or drug candidates, the concept of drug-likeness has been widely used to screen out compounds with undesirable properties, particularly those with poor ADMET (absorption, distribution, metabolism, excretion, and toxicity) profiles [23; 28; 29; 30; 31; 32; 33]. With varying goals, new computational algorithms and analytical techniques are being created as the volume of biological data continues to increase. This field covers a broad range of topics, including medication toxicity prediction and protein structure prediction. The majority of semi-empirical force-field- and quantum-mechanics-based molecular modeling methods demonstrate proven accuracy in analyzing small structural datasets, while statistics-based methods like machine learning, QSAR, and other specialized data analytics methods are robust for large-scale data analysis [34]. As a conclusion, modern drug discovery methodologies and technologies have had a significant impact on the increasing number of first-in-class pharmaceuticals approved in recent years, in line with the pharmaceutical industry's drive to find breakthrough therapies [35; 36]. This research received no external funding. The authors declare no conflict of interest.