Design of new Mcl-1 inhibitors for cancer using fragments hybridization, molecular docking, and molecular dynamics studies
Bibliographic record
Abstract
Apoptosis is a critical process that regulates cell survival and death and plays an essential role in cancer development. The Bcl-2 protein family, including myeloid leukemia 1 (Mcl-1), is a key regulator of the intrinsic apoptosis pathway, and its overexpression in many human cancers has prompted efforts to develop Mcl-1 inhibitors as potential anticancer agents. In this study, we aimed to design new Mcl-1 inhibitors using various computational techniques. First, we used the Mcl-1 receptor-ligand complex to build an e-pharmacophore hypothesis and screened a library of 567,000 fragments from the Enamine database. We obtained 410 fragments and used them to design 92,384 novel compounds, which we then docked into the Mcl-1 binding cavity using HTVS, SP, and XP docking modes of Glide. To assess their suitability as drug candidates, we conducted MM-GBSA calculations and ADME prediction, leading to the identification of 10 compounds with excellent binding affinity and favorable pharmacokinetic properties. To further investigate the interaction strength, we performed molecular dynamics simulations on the top three Mcl-1 receptor-ligand complexes to study their interaction stability. Overall, our findings suggest that these compounds have promising potential as anticancer agents, pending further experimental validation such as Mcl-1 apoptosis Assay. By combining experimental methods with various in silico approaches, these techniques prove to be invaluable for identifying novel drug candidates with distinct therapeutic applications using fragment-based drug design. This methodology has the potential to expedite the drug discovery process while also reducing its costs.Communicated by Ramaswamy H. Sarma
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".