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<p>The development of anticancer drugs, a time-consuming and costly process, faces challenges that new technologies in drug discovery aim to overcome. Computer-aided methods, particularly virtual screening techniques like structure-based and ligand-based approaches, expedite the identification of potential anticancer agents. Molecular dynamics simulation provides insights into drug molecule behavior, while predictive modeling assesses combination therapies and pharmacophore modeling guides drug design. Chemoinformatics techniques analyze chemical data, and high-throughput screening automates the testing of compound libraries. Network pharmacology examines immune system interactions for potential drug targets. The integration of these computational methods accelerates drug discovery and contributes to the development of more effective and targeted anticancer therapies. AI further enhances cancer research by analyzing multiomics data, aiding in the identification of novel anticancer targets and drugs. The study emphasizes the potential of AI in advancing therapeutic development. FDA-approved inhibitors and targeted therapies, including CTLA-4 and PD-1 inhibitors, showcase significant progress in cancer clinical research. Personalized vaccines and combination therapies, guided by prediction algorithms, mark breakthroughs in cancer immunotherapy. The utilization of immune checkpoint inhibitors has transformed cancer treatment, and ongoing research explores additional checkpoint proteins as potential adjuvant drugs. The multifaceted approach holds promise for treating various cancer types and improving patient survival, highlighting the dynamic landscape of immunotherapeutic interventions.</p>

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Advances in cancer therapy: unveil the immunomodulatory protein involved in signaling pathways as molecular targets

  • 期刊论文

  • 发表日期：

    2025年3月24日

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  • Chandrabose Selvaraj,
  • Rajendran Santhosh,
  • Abdulaziz S. Alothaim,
  • Rajendran Vijayakumar,
  • Dipali Desai,
  • Sher Zaman Safi,
  • Sanjeev Kumar Singh

摘要

The development of anticancer drugs, a time-consuming and costly process, faces challenges that new technologies in drug discovery aim to overcome. Computer-aided methods, particularly virtual screening techniques like structure-based and ligand-based approaches, expedite the identification of potential anticancer agents. Molecular dynamics simulation provides insights into drug molecule behavior, while predictive modeling assesses combination therapies and pharmacophore modeling guides drug design. Chemoinformatics techniques analyze chemical data, and high-throughput screening automates the testing of compound libraries. Network pharmacology examines immune system interactions for potential drug targets. The integration of these computational methods accelerates drug discovery and contributes to the development of more effective and targeted anticancer therapies. AI further enhances cancer research by analyzing multiomics data, aiding in the identification of novel anticancer targets and drugs. The study emphasizes the potential of AI in advancing therapeutic development. FDA-approved inhibitors and targeted therapies, including CTLA-4 and PD-1 inhibitors, showcase significant progress in cancer clinical research. Personalized vaccines and combination therapies, guided by prediction algorithms, mark breakthroughs in cancer immunotherapy. The utilization of immune checkpoint inhibitors has transformed cancer treatment, and ongoing research explores additional checkpoint proteins as potential adjuvant drugs. The multifaceted approach holds promise for treating various cancer types and improving patient survival, highlighting the dynamic landscape of immunotherapeutic interventions.