Computational approaches: atom-based 3D-QSAR, molecular docking, ADME-Tox, MD simulation and DFT to find novel multi-targeted anti-tubercular agents

Table 6 Predictions of drug-likeness and rule of five for the top screened compounds

Compound ID	MW	Vol	LogP	nHA	TPSA	nRot	Synth	MCE-18	Lipinski
MK1	409.1	355.618	2.955	9	97.24	6	3.454	78.545	Accepted
MK2	409.1	355.618	3.329	9	97.24	6	3.413	78.545	Accepted
MK3	411.09	343.02	2.018	11	123.02	6	3.617	79.2	Accepted
MK4	360.1	319.888	1.876	10	110.13	5	3.524	69.632	Accepted
MK5	410.1	349.319	2.825	10	110.13	6	3.59	78.857	Accepted
MK6	360.1	319.888	1.311	10	110.13	5	3.415	69.632	Accepted
MK7	410.1	349.319	2.435	10	110.13	6	3.49	78.857	Accepted
MK8	342.11	313.82	1.847	10	110.13	5	3.481	66.316	Accepted
MK9	367.1	336.84	1.047	11	133.92	5	3.514	69.3	Accepted

MW Molecular weight (≤ 500), Vol vander Waal’s volume, LogP Distribution coefficient (≤ 5), nHA Hydrogen bond acceptor(0–12), nHD Hydrogen bond donor (≤ 5), TPSA Topological Polar Surface area( < 140), nROT Number of rotatable bond (0–11), Synth Synthetic accessibility Score (1–6 (excellent), > 6 (poor)), MEC-18 Medicinal chemistry Evaluation 2018 (≥ 45 excellent)

ISSN: 2661-801X