In silico ligand‐based modeling of hBACE‐1 inhibitors

Abstract

Alzheimer's disease is a chronic neurodegenerative disease affecting more than 30 million people worldwide. Development of small molecule inhibitors of human β-secretase 1 (hBACE-1) is being the focus of pharmaceutical industry for the past 15-20 years. Here, we successfully applied multiple ligand-based in silico modeling techniques to understand the inhibitory activities of a diverse set of small molecule hBACE-1 inhibitors reported in the scientific literature. Strikingly, the use of only a small subset of 230 (13%) molecules allowed us to develop quality models that performed reasonably well on the validation set of 1,476 (87%) inhibitors. Varying the descriptor sets and the complexity of the modeling techniques resulted in only minor improvements to the model's performance. The current results demonstrate that predictive models can be built by choosing appropriate modeling techniques in spite of using small datasets consisting of diverse chemical classes, a scenario typical in triaging of high-throughput screening results to identify false negatives. We hope that these encouraging results will help the community to develop more predictive models that would support research efforts for the debilitating Alzheimer's disease. Additionally, the integrated diversity of the techniques employed will stimulate scientists in the field to use in silico statistical modeling techniques like these to derive better models to help advance the drug discovery projects faster.

Recent Developments in New Therapeutic Agents against Alzheimer and Parkinson Diseases: In-Silico Approaches

“…In particular, compound AC4 (Figure 8), a phenotiazine-chalcone derivative, displayed the highest affinity towards the target [62]. A distinct procedure was carried out by Subramanian and co-workers, using LBDD to construct a predictive model for small BACE-1 inhibitor molecules [63]. Gathering all the small inhibitors used to co-crystallize all the existing crystal structures of BACE-1, the team built 1-/2-and 3D-field descriptors and applied machine learning techniques to classify potential BACE-1 inhibitors [63].…”

“…A distinct procedure was carried out by Subramanian and co-workers, using LBDD to construct a predictive model for small BACE-1 inhibitor molecules [63]. Gathering all the small inhibitors used to co-crystallize all the existing crystal structures of BACE-1, the team built 1-/2-and 3D-field descriptors and applied machine learning techniques to classify potential BACE-1 inhibitors [63]. On the other hand, Thai et al validated a 2D-QSAR model built out of pharmacophoric 3D-models based on the structure of clinically used drugs and compounds in clinical trials to evaluate novel curcumin and flavonoids derivatives (Figure 8) based on their potential to inhibit BACE-1 [64].…”