Tubulin Polymerization Inhibitor Design

Microtubules are an essential part of cellular structure and are involved in many important cellular processes. One of the most important actions of antivascular drugs is to disrupt tubulin-microtubule dynamics, induce mitotic arrest and lead to cell death. Therefore, tubulin has also been a major target for anticancer drug discovery over the past few decades. Tubulin has three distinct small molecule binding sites, the taxane-binding site, the vinblastine-binding site, and the colchicine-binding site. Taxanes and vinca alkaloids have been successfully used in the clinical treatment of cancer. Dual-target tubulin inhibitors that regulate multiple tumor-related targets have attracted the attention of scientists. In addition to tubulin-c-MET dual-target inhibitors and tivantinib, we are also working hard to study tubulin and Hsp90 A dual-target inhibitor of targets such as , Src, PI3K, TDO/IDO and topoisomerase.

Tubulin Polymerization Inhibitor Design

Tubulin Polymerization as Targets for Developing Anticancer Drugs

Microtubules are cytoskeletal filaments that assemble from and disassemble into their αβ-tubulin isoforms. Since microtubules are involved in several important cellular activities, including cell division, cell motility, and intracellular transport, drugs that interfere with microtubule function have been very successfully developed as chemotherapeutic agents against different malignancies.

Tubulin Polymerization Inhibitor Design Strategies

  • Dual-target inhibitors targeting microtubules
    Designing dual-target tubulin inhibitors is an effective way to overcome drug resistance and improve efficacy. Therefore, scientists at CD ComputaBio combined tubulin inhibitors with other anti-tumor drugs to have a synergistic effect.

    Tubulin Polymerization Inhibitor Design

    Since the design of dual-target drugs is more complex than that of single-target drugs, our scientists use various design strategies, including drug repurposing, backbone design of dual-target inhibitors, pharmacophore-based combination and Computational methods to have further enhanced the development of dual-target drugs.
  • AI-Bind for Protein-ligand Binding Prediction
    This method uses the network sampling strategy to increase negative samples to reduce the impact of sample imbalance. Experiments have proved that the model under this framework can also perform better than the mainstream framework in the prediction of proteins and ligands not included in the training set.
    We used the predictive performance of the two models in three different scenarios:
    (1) The training set contains the proteins and ligands of the test set.
    (2) The training set only contains the ligands of the test set.
    (3) The training set does not contain the proteins and ligands of the test set.
  • AI-Bind Model Performance Verification
    After the balanced sample training after network sampling, the performance of the three models is verified. Due to the elimination of sample imbalance, the experimental method of the control group has improved the binding prediction performance of new proteins and ligands, while AI-Bind has the best performance in the binding prediction of new proteins and ligands.

Features of Our Dual-target Inhibitors Design

In addition to applying traditional drug discovery strategies, our scientists continue to use many new methods for the rational design of dual-target inhibitors, for example, by predicting the structural similarity between the tubulin binding pocket and other anti-tumor targets Signal network analysis, computer pharmacophore modeling, artificial intelligence technology, etc.

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If you have service needs for drug design projects, please feel free to contact us.

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