In silicoexploration of the fingerprints triggering modulation of glutaminyl cyclase inhibition for the treatment of Alzheimer’s disease using SMILES based attributes in Monte Carlo optimization

Development and evolution of human glutaminyl cyclase inhibitors (QCIs): an alternative promising approach for disease-modifying treatment of Alzheimer's disease

“…Similarly, Ashwani Kumar et al identified the structural features that are both positively and negatively responsible for the QC inhibitory activity based on a dataset of 125 QCIs for QSAR analysis via Monte Carlo modeling studies. The QSAR further supports the importance of 5-methy substituted imidazole and alkyl-substituted benzene in activity enhancement, as previous SAR revealed, and novel compounds 30-32 (Table 3) were then computationally designed and showed improved pKi and QC binding affinities (Kumar et al, 2021). The hits of the two studies actually inherited typical features of classic QCIs with imidazole or methyl-imidazole as ZBG and an aromatic group located in the opposite position, while the QC inhibitory activities were not experimentally evaluated and validated in vitro.…”

“…In addition to the various rational design and experimentalbased QCI discoveries, virtual screening offers another efficient tool to advance the understanding of activity profiles, and the development of new QCIs (Kumar et al, 2013;Lin et al, 2019). Those screening strategies include fragment-based screening (Szaszko et al, 2017), QSAR modeling (Al-Attraqchi andVenugopala, 2020;Kumar et al, 2021), and pharmacophoreassisted high-throughput virtual screening (Lin et al, 2019). Katharigatta N. Venugopala et al developed linear and non-linear 2D QSAR models and a partial least squares-based 3D model to help predict the activity of not yet synthesized compounds.…”

“…PQ912 has strong human, rat, and mouse QC inhibitory activity with K i values ranging between 20 and 65 nM (Hoffmann et al, 2017). Preclinical studies revealed that PQ912 has an attractive drug-like profile and 27-29 (Al-Attraqchi and Venugopala, 2020) 30-32 (Kumar et al, 2021) 33 (Dileep et al, 2021) 34-36 (Xu et al, 2020) robust pharmacological therapeutic effects, both in vitro and in vivo (Hoffmann et al, 2017). PQ912 was considered safe and well tolerated with dose-proportional pharmacokinetics up to doses of 200 mg in the first-in-man phase 1 study (Lues et al, 2015).…”

Prediction of second-order rate constants of the sulfate radical anion with aromatic contaminants using the Monte Carlo technique

“…In Monte Carlo optimization, the numerical values of T and N are searched to obtain the best statistical quality for the calibration set. 49–51 So, the hybrid optimal descriptor applied here is computed according to the following equations: Hybrid DCW( T *, N *) = DCW SMILES ( T *, N *) + DCW HFG ( T *, N *), SMILES DCW( T *, N *) = ∑CW(SSSk) + CW(NOSP) + CW(HALO) + CW(HARD) + CW(BOND)and HFG DCW( T *, N *) = ∑CW(EC2k) + ∑CW(Ep2k) + ∑CW(pt3k) + ∑CW(VS2k) + ∑CW(nnk).The explanation for the notations utilized in eqn (3) and (4) is given in Table S2 (ESI†). CW(X) demonstrates the correlation weight for the SMILES and graph attributes (X = SSS k , BOND, HALO, NOSP, HARD, or e1 k , p2 k , p3 k , VS2 k and nn k ).…”

“…In Monte Carlo optimization, the numerical values of T and N are searched to obtain the best statistical quality for the calibration set. [49][50][51] So, the hybrid optimal descriptor applied here is computed according to the following equations:…”