Longer timelines and lower success rates of drug candidates limit the
productivity of clinical trials in the pharmaceutical industry. Promising de
novo drug design techniques help solve this by exploring a broader chemical
space, efficiently generating new molecules, and providing improved therapies.
However, optimizing for molecular characteristics found in approved oral drugs
remains a challenge, limiting de novo usage. In this work, we propose NovoMol,
a novel de novo method using recurrent neural networks to mass-generate drug
molecules with high oral bioavailability, increasing clinical trial time
efficiency. Molecules were optimized for desirable traits and ranked using the
quantitative estimate of drug-likeness (QED). Generated molecules meeting QED's
oral bioavailability threshold were used to retrain the neural network, and,
after five training cycles, 76% of generated molecules passed this strict
threshold and 96% passed the traditionally used Lipinski's Rule of Five. The
trained model was then used to generate specific drug candidates for the
cancer-related PDGFR{\alpha} receptor and 44% of generated candidates had
better binding affinity than the current state-of-the-art drug, Imatinib (with
a receptor binding affinity of -9.4 kcal/mol), and the best-generated candidate
at -12.9 kcal/mol. NovoMol provides a time/cost-efficient AI-based de novo
method offering promising drug candidates for clinical trials.

提出了一种使用循环神经网络的新型去 novo 方法，用于大规模生成具有高口服生物利用度的药物分子，从而提高临床试验时间效率。

NovoMol: 口服可生物利用药物设计及在 PDGFRα 受体上的验证

NovoMol: Recurrent Neural Network for Orally Bioavailable Drug Design  and Validation on PDGFRα Receptor

Multi-view clustering methods are essential for the stratification of
patients into sub-groups of similar molecular characteristics. In recent years,
a wide range of methods has been developed for this purpose. However, due to
the high diversity of cancer-related data, a single method may not perform
sufficiently well in all cases. We present Parea, a multi-view hierarchical
ensemble clustering approach for disease subtype discovery. We demonstrate its
performance on several machine learning benchmark datasets. We apply and
validate our methodology on real-world multi-view cancer patient data. Parea
outperforms the current state-of-the-art on six out of seven analysed cancer
types. We have integrated the Parea method into our developed Python package
Pyrea (this https URL), which enables the effortless and
flexible design of ensemble workflows while incorporating a wide range of
fusion and clustering algorithms.

Parea 是一种多视角层次集成聚类方法，适用于疾病亚型发现，已成功应用于多种机器学习基准数据集和真实的多视角癌症患者数据，在七种癌症类型中有六个表现优于当前最先进的方法，已被集成到 Python 包 Pyrea 中，可轻松灵活地设计集成工作流程，并整合了各种融合和聚类算法。