Due to the deadly outbreak of the coronavirus in recent times, the dependency on drugs and drug solutions tremendously increased. Some countries had the access to the necessary drugs to curb the virus, while some others didn’t. It became quite haphazard to reduce the impact of the virus worldwide as the drug agencies in the world got involved in finding solutions.
Artificial intelligence has been quite helpful since. AI approaches are now used to aid the drug design process and provide for simple methods of drug solutions. Computer-aided drug design (CADD) works on 2 approaches mainly:
• Structure-based drug design (SBDD)
• Ligand-based drug design (LBDD)
Antibiotics drug-resistant are quite heavily used for treating diseases. Now, computational approaches are being used for the same. For designing new antibiotics, computer-aided drug design (CADD) can be combined with wet-lab techniques. This elucidates the mechanism of drug resistance and searches for new antibiotic targets to design novel antibiotics for both known and new targets. These CADD methods are useful in producing atomic-level structure-activity relationships and minimize the time for facilitating the drug design process.
The antibiotic resistance issue could also be solved by the identification of new antibiotic targets that may represent conditions essential for bacterial survival. According to studies, the CADD has also helped the researchers in analyzing new antibiotics against existing targets.
The methods involved in CADD are:
• MD simulations: could be used to study target-ligand interactions at an atomic level & to generate points for the target or for the ligand to take flexibility for both SBDD and LBDD studies. They also help to estimate the relative free energies of binding.
• SILCS: Site Identification by Ligand Competitive Saturation: It uses all-atom explicit-solvent MD simulations to identify 3D functional-group binding patterns on the target. Then these patterns are used to direct ligand design. When converted to free energies, termed grid free energy (GFE) is used to estimate the relative binding affinities of ligands.
• Database preparation: it is an efficient way to find potential low-molecular-weight binders to the target protein.
• Docking-based VS: it implies placing a compound in the putative binding site on the target in an optimal way in combination with a conformational sampling method.
• SILCS Pharm: it is a replacement for docking-based VS. It can quickly filter a database for potential binders to a specific bacterial target. It represents a simplification of the detailed energetic information used by docking methods and so its computational needs are much lower.
There are various other methods too. Refer to here, for more information.
