Computer-aided drug discovery strategies for novel therapeutics for prostate cancer leveraging next-generating sequencing data

Weijie Zhang, R. Stephanie Huang

Research output: Contribution to journalReview articlepeer-review


Introduction: Prostate cancer (PC) is the most common malignancy and accounts for a significant proportion of cancer deaths among men. Although initial therapy success can often be observed in patients diagnosed with localized PC, many patients eventually develop disease recurrence and metastasis. Without effective treatments, patients with aggressive PC display very poor survival. To curb the current high mortality rate, many investigations have been carried out to identify efficacious therapeutics. Compared to de novo drug designs, computational methods have been widely employed to offer actionable drug predictions in a fast and cost-efficient way. Particularly, powered by an increasing availability of next-generation sequencing molecular profiles from PC patients, computer-aided approaches can be tailored to screen for candidate drugs. Areas covered: Herein, the authors review the recent advances in computational methods for drug discovery utilizing molecular profiles from PC patients. Given the uniqueness in PC therapeutic needs, they discuss in detail the drug discovery goals of these studies, highlighting their translational values for clinically impactful drug nomination. Expert opinion: Evolving molecular profiling techniques may enable new perspectives for computer-aided approaches to offer drug candidates for different tumor microenvironments. With ongoing efforts to incorporate new compounds into large-scale high-throughput screens, the authors envision continued expansion of drug candidate pools.

Original languageEnglish (US)
Pages (from-to)841-853
Number of pages13
JournalExpert Opinion on Drug Discovery
Issue number7
StatePublished - 2024

Bibliographical note

Publisher Copyright:
© 2024 Informa UK Limited, trading as Taylor & Francis Group.


  • castration-resistant prostate cancer
  • Computational drug discovery
  • drug repurposing
  • molecular profiling
  • next-generation sequencing
  • prostate cancer
  • prostate cancer transcriptomics

PubMed: MeSH publication types

  • Journal Article
  • Review


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