Dear Niazi,

The molecular design is increasingly marked by computer simulations, in hardware advances and the findings of new algorithms. Although they are a support for the experimental chemist, among the advantages of these analysis techniques is that they help to predict new mechanisms; but they also represent disadvantages (especially when simulation is done without experience in experimental chemistry).

Some references about the future of molecular design:

Sneha, P., and C. George Priya Doss. "Molecular dynamics: new frontier in personalized medicine." Advances in protein chemistry and structural biology. Vol. 102. Academic Press, 2016. 181-224.

https://doi.org/10.1016/bs.apcsb.2015.09.004

Raantanen, Jukka, and Johannes Khinast. "The future of pharmaceutical manufacturing sciences." Journal of pharmaceutical sciences 104.11 (2015): 3612-3638.

https://doi.org/10.1002/jps.24594

Borhani, David W., and David E. Shaw. "The future of molecular dynamics simulations in drug discovery." Journal of computer-aided molecular design 26.1 (2012): 15-26.

https://doi.org/10.1007/s10822-011-9517-y

Best regards,

Ender Araujo

Improving QSAR methodology. There is an easy way using another geometric perrspective. That is, taking as a firs step to get rid of the dimensionality paradox, and shift to a description of the relatioship problems from parameter space to molecular space. The second step is to get rid of arbitrar parameters and adopt a quantum similarity point of view. Third, but not finally, as it is plenty research field to walk, adopt a quantum point of view and try to define a QSAR operator to built in the QSAR equations.

Dear Niazi,

The molecular design will be more inclined to embrace a transcriptomics-based approach that centered on RNA-RNA, RNA-DNA, and RNA-Protein interaction studies. The methods to predict 2D and 3D structures of RNA are already determined as shown in this reference:

Parikesit, A. A. (2018). The Construction of Two and Three Dimensional Molecular Models for the miR-31 and Its Silencer as the Triple Negative Breast Cancer Biomarkers. OnLine Journal of Biological Sciences, 18(4), 424–431. https://doi.org/10.3844/ojbsci.2018.424.431

Parikesit, A. A., Utomo, D. H., & Karimah, N. (2018). Determination of secondary and tertiary structures of cervical cancer lncRNA diagnostic and siRNA therapeutic biomarkers. Indonesian Journal of Biotechnology, 23(1), 1. https://doi.org/10.22146/ijbiotech.28508

In this respect, a design of silencing (si)RNA molecules as drug candidates will gain more importance in the future, as well as studies of long(ln)RNA biomarkers. Keep in mind that although transcriptomics-based approach will gain importance, the proteomics-based one will still be utilized by molecular designers.