PanScreen: A Comprehensive Approach to Off-Target Liability Assessment
Sellner, M. S.; Joos, F. L.; Odermatt, A.; Lill, M. A.; Smiesko, M. · bioinformatics · 2026-09-06 · 原文
DOI：10.1101/2023.11.16.567496作者：5 位
Drug development projects are getting increasingly more expensive while their success rate is stagnating. Safety issues attributed to off-target binding represent a major reason for the failure of new drugs. Besides desired on-target binding, small molecules may interact with off-targets, triggering adverse effects. Therefore, the development of novel methods for early recognition of such issues that are resource-efficient and cost-effective becomes vital. Here, we introduce PanScreen, an online platform for the automated assessment of off-target liabilities. PanScreen combines structure-based modeling techniques with state-of-the-art deep learning methods to not only predict accurate binding affinities but also give insight into potential modes of action. We show that the predictions are approaching experimental accuracy found in public datasets and that the same technology can also be used for other research areas, such as drug repurposing. Such fast and inexpensive methods allow researchers to test not only drug candidates, but all small molecules that might come into contact with a human organism for potential safety concerns very early in the development process. PanScreen is
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1. 人话版
Drug development projects are getting increasingly more expensive while their success rate is stagnating.
Safety issues attributed to off-target binding represent a major reason for the failure of new drugs.
2. 领域脉络
本文类目：bioinformatics，属于其所在研究脉络的最新进展。
3. 机制拆解
Besides desired on-target binding, small molecules may interact with off-targets, triggering adverse effects.
Therefore, the development of novel methods for early recognition of such issues that are resource-efficient and cost-effective becomes vital.
Here, we introduce PanScreen, an online platform for the automated assessment of off-target liabilities.
4. 证据与数字
摘要未给出量化结果——留意原文的实验与数据。
5. 反例与边界
PanScreen combines structure-based modeling techniques with state-of-the-art deep learning methods to not only predict accurate binding affinities but also give insight into potential modes of action.
Such fast and inexpensive methods allow researchers to test not only drug candidates, but all small molecules that might come into contact with a human organism for potential safety concerns very early in the development process.
6. 跨领域连接与意外收获
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7. 可复用方法
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8. 术语表
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