Apo2ph4: A Versatile Workflow for the Generation of Receptor-based Pharmacophore Models for Virtual Screening

Abstract

Pharmacophore models are widely used as efficient virtual screening (VS) filters for the target-directed enrichment of large compound libraries. However, the generation of pharmacophore models that have the power to discriminate between active and inactive molecules traditionally requires structural information about ligand−target complexes or at the very least knowledge of one active ligand. The fact that the discovery of the first known active ligand of a newly investigated target represents a major hurdle at the beginning of every drug discovery project underscores the need for methods that are able to derive high-quality pharmacophore models even without the prior knowledge of any active ligand structures. In this work, we introduce a novel workflow, called apo2ph4, that enables the rapid derivation of pharmacophore models solely from the three-dimensional structure of the target receptor. The utility of this workflow is demonstrated retrospectively for the generation of a pharmacophore model for the M2 muscarinic acetylcholine receptor. Furthermore, in order to show the general applicability of apo2ph4, the workflow was employed for all 15 targets of the recently published LIT-PCBA dataset. Pharmacophore-based VS runs using the apo2ph4-derived models achieved a significant enrichment of actives for 13 targets. In the last presented example, a pharmacophore model derived from the etomidate site of the α1β2γ2 GABA A receptor was used in VS campaigns. Subsequent in vitro testing of selected hits revealed that 19 out of 20 (95%) tested compounds were able to significantly enhance GABA currents, which impressively demonstrates the applicability of apo2ph4 for real-world drug design projects.

Amomum subulatum: A treasure trove of anti-cancer compounds targeting TP53 protein using in vitro and in silico techniques

“…com/). We select phytocompounds based on their pharmacophore fit score using AutoDock Vina and employed LigandScout for pharmacophore modelling (Wolber and Langer, 2005;Heider et al, 2022). We utilized AutoDock Vina for targeted molecular docking and meticulously examined and visualized the docking investigations employing UCSF Chimera v1.12 and Discovery Studio.…”

Watermelon: setup and validation of an in silico fragment-based approach

“…In this specific protocol, the docking procedure is then followed by an energy minimisation step, at the end of which the minimised fragments are clustered and hotspots are defined as consensus sites where probes clusters resulted to overlap. In another recently developed workflow, namely apo2ph4, the docking procedure is performed using several drug-like fragments 4 . In this method, each fragment-receptor complex is directly submitted to LigandScout pharmacophore modelling software 5 to retrieve pharmacophoric features; subsequently, the final pharmacophore model to be used for VS is obtained by clustering, scoring and filtering all the generated features.…”

Discovery of novel JAK1 inhibitors through combining machine learning, structure-based pharmacophore modeling and bio-evaluation

“…Pharmacophore models describe the vital molecular features and their spatial arrangement of ligand–protein interactions and are a fast and efficient method for virtual screening (VS) active drug molecules [ 19 , 20 ]. Despite the significant advances, the pharmacophore approach still faces several challenges, such as the low efficiency of screening large chemical databases with flexible molecules and high false positive/negative rates due to model quality issues [ 21 , 22 ].…”

“…Recently, sophisticated deep learning methods have been applied in VS due to their high recall and low false-positive rates, and could be combined with other methods to develop more efficient and accurate VS methods to discover novel active molecules [26][27][28]. However, as far as we know, research on ML predictive models for VS of kinase inhibitors was quite limited, and lacked bioactivity validation [5,20,29,30]. Therefore, combining pharmacophore and ML models is necessary to build a powerful integrated model to screen potential JAK1 inhibitors.…”