The Importance of Biological Invariance in Drug Design

Drug design and discovery is one of the forefront challenges within the pharmaceutical industries, the process which potential new chemicals are identified by means of pharmacology. Since understanding diseases and its potential drugs is an arduous task, the use of Artificial Intelligence has become an important asset. Biological invariance has arisen as a new paradigm in genomics and drug design, since the analysis of the genomics and potential compounds should be independent of the sampling methodology and the classifier utilized for their inference. Predominantly, phenotype prediction and the analysis of altered pathways has become an important discipline in drug discovery and precision medicine, that is, finding a set of genes that prospectively differentiates a specified phenotype disease with respect to a control sample. The problem is highly undetermined since the number of monitored genetic probes exceeds the number of samples, therefore, this creates an ambiguity in the characterization that must be overcome by the use of AI and deep sampling techniques, which ultimately help the drug repurposing and speeding up the drug selection process.

Drug design and discovery is one of the forefront challenges within the pharmaceutical industries, the process which potential new chemicals are identified by means of pharmacology. Since understanding diseases and its potential drugs is an arduous task, the use of Artificial Intelligence has become an important asset. Biological invariance has arisen as a new paradigm in genomics and drug design, since the analysis of the genomics and potential compounds should be independent of the sampling methodology and the classifier utilized for their inference. Predominantly, phenotype prediction and the analysis of altered pathways has become an important discipline in drug discovery and precision medicine, that is, finding a set of genes that prospectively differentiates a specified phenotype disease with respect to a control sample. The problem is highly undetermined since the number of monitored genetic probes exceeds the number of samples, therefore, this creates an ambiguity in the characterization that must be overcome by the use of AI and deep sampling techniques, which ultimately help the drug repurposing and speeding up the drug selection process.
the results, would lead to the same results, as reported by Cernea et al. [3]. Consequently, biological invariance, a concept that implies that the high discriminatory genetic networks are located within the neighborhood of Mtol and the altered pathways are independent of the classifier and the sampling method utilized to unravel them, would help in the drug design and repositioning process.

Drug Design and Repositioning
After a robust sampling of the genetic pathways, an optimum selection of potential drugs can be carried out. This methodology will dramatically increase the approval of drugs and reposition the existing ones. In the present days, there are a wide range of models utilized in drug design, from models based on large data sets of chemicals, compositions and disease-drug activity, known as perturbation models [12], to multiscale models that are capable of integrating different genomic, proteomic and metabolomic information [13]. Furthermore, a new paradigm known as de novo multiscale modelling has emerged, which allows the design of a new drug within the chemical subspace where it could be beneficious [14]. However, regardless of the methodology utilized, powerful AI tools are required in order to integrate information from different types of drugs to biological data and disease specific network studies that serve to identify new potential target [15]. In addition, AI shall be utilized to determine from complex genetic data and all the research information available the interaction a drug may have with its primary target (that tackle the disease) and secondary targets (that causes side-effects). This process is supposed to be, such as in the sampling of altered pathways, independent from the methodology.

Conclusion
Further research and development of AI methods are required to confirm the hypothesis of Biological Invariance, however, it offers a tantalizing approach to handle this complex and difficult problem in the pharmaceutical industry. The mentioned concept would enable the design of affordable genetic tools that would enhance the precision medicine. However, further confirmation is required, being the absence of data one of the major drawback.
Biological invariance suggests that any AI protocol to enhance drug design should be iterative and learn from experience. Algorithms and methodologies shall be kept simple and fast, since they would yield to the same results and with the same accuracy.