The drug discovery process typically involves target identification and design of suitable drug molecules against these targets. Despite decades of experimental investigations in the drug discovery domain, about 96% overall failure rate has been recorded in drug development due to the “undruggability” of various identified disease targets, in addition to other challenges. Likewise, the high attrition rate of drug candidates in the drug discovery process has also become an enormous challenge for the pharmaceutical industry. To alleviate this negative outlook, new trends in drug discovery have emerged. By drifting away from experimental research methods, computational tools and big data are becoming valuable in the prediction of biological target druggability and the drug-likeness of potential therapeutic agents. These tools have proven to be useful in saving time and reducing research costs. As with any emerging technique, however, controversial opinions have been presented regarding the validation of predictive computational tools. To address the challenges associated with these varying opinions, this review attempts to highlight the principles of druggability and drug-likeness and their recent advancements in the drug discovery field. Herein, we present the different computational tools and their reliability of predictive analysis in the drug discovery domain. We believe that this report would serve as a comprehensive guide towards computational-oriented drug discovery research.

Highlights of methods for assessing the druggability of biological targets

中文翻译：

---

药物设计中的可药物性和相似性概念：生物建模和预测性工具有发言权吗？

药物发现过程通常涉及目标识别和针对这些目标的合适药物分子设计。尽管在药物发现领域进行了数十年的实验研究，但除其他挑战外，由于各种已确定疾病目标的“不可负担性”，药物开发中已记录了约96％的总失败率。同样，药物发现过程中候选药物的高损耗率也已成为制药行业的巨大挑战。为了减轻这种负面前景，出现了新的药物发现趋势。通过摆脱实验研究方法，计算工具和大数据在预测生物靶标药物可控性和潜在治疗剂的药物相似性方面变得有价值。这些工具已被证明在节省时间和降低研究成本方面很有用。但是，与任何新兴技术一样，已经提出了有关预测计算工具验证的争议性意见。为了解决与这些不同意见相关的挑战，本综述试图强调可药物性和药物相似性的原理及其在药物发现领域的最新进展。在本文中，我们介绍了不同的计算工具及其在药物发现领域进行预测分析的可靠性。我们相信，该报告将作为面向计算的药物发现研究的综合指南。关于预测计算工具的验证，已经提出了有争议的意见。为了解决与这些不同意见相关的挑战，本综述试图强调可药物性和药物相似性的原理及其在药物发现领域的最新进展。在这里，我们介绍了不同的计算工具及其在药物发现领域进行预测分析的可靠性。我们相信，该报告将作为面向计算的药物发现研究的综合指南。关于预测计算工具的验证，已经提出了有争议的意见。为了解决与这些不同意见相关的挑战，本综述试图强调可药物性和药物相似性的原理及其在药物发现领域的最新进展。在这里，我们介绍了不同的计算工具及其在药物发现领域进行预测分析的可靠性。我们相信，该报告将作为面向计算的药物发现研究的综合指南。我们介绍了不同的计算工具及其在药物发现领域进行预测分析的可靠性。我们相信，该报告将作为面向计算的药物发现研究的综合指南。我们介绍了不同的计算工具及其在药物发现领域进行预测分析的可靠性。我们认为，该报告将作为面向计算的药物发现研究的综合指南。

评估生物靶标可药用性的方法的重点