MegaTrans – human transporter machine learning models

摘要 能够预测与重要的人类转运蛋白的相互作用将对新药设计具有价值 避免与它们相互作用并引起不良副作用的化合物。OATP1B1(SLCO1B1)和 OATP1B3(SLCO1B3)是一种摄取转运蛋白，主要局限于肝细胞的肝窦方面。他们 两者都运输各种各样的结构无关的化合物，包括几个临床上不相关的成员。 重要的药物家族，如他汀类、沙坦和血管紧张素转换酶(ACE)抑制剂。我们现在 建议在体外针对每个转运体的两种底物测试1000多种药物。然后我们将使用这些数据来 管理和验证机器学习模型。我们还将使用一系列机器学习方法以及 多个模型评估指标。这将使我们能够开发一个基于Web的软件工具，称为MegaTrans 这将鼓励用户输入他们自己的复合结构并生成对交互的预测 使用感兴趣的Transporter/S，然后使用几个 不同的可视化方法。这样一个工具的投资回报将是它可以帮助设计 选择更有利的化合物，避免了感兴趣的转运体，同时也节省了时间和金钱。 它还可以识别已经批准的可能存在药物相互作用风险的化合物。预测 在体内看到的这种行为是理想的，并将导致对化合物进行优先排序，以在体外测试其潜力 药物之间的相互作用。在第二阶段，我们将极大地扩大我们将产生的运输机的数量 并建立模型，以便我们能够解决所有与药物发现有关的主要转运体。

Summary Being able to predict interactions with important human transporters would be of value to new drug design to avoid compounds that interact with them and cause undesirable side effects. OATP1B1 (SLCO1B1) and OATP1B3 (SLCO1B3) are `uptake' transporters largely restricted to the sinusoidal aspect of hepatocytes. They both transport a wide variety of structurally-unrelated compounds, including members of several clinically im- portant drug families such as statins, sartans and angiotensin converting enzyme (ACE) inhibitors. We now propose to test over 1000 drugs against 2 substrates for each transporter in vitro. We will then use these data to curate and validate machine learning models. We will also use an array of machine learning methods as well as multiple model evaluation metrics. This will enable us to develop a web-based software tool called MegaTrans that will encourage the user to input their own compound structures and generate predictions for interactions with transporter/s of interest and then visualize the similarity to the training set of each model using several different visualization methods. The return on investment of such a tool would be that it could assist in the design and selection of more favorable compounds that avoid transporters of interest while also saving time and money. It could also identify compounds that are already approved that might present a drug interaction risk. Predicting such behavior seen in vivo is ideal and will lead to the prioritization of compounds to test in vitro for potential drug-drug interactions. In Phase II we would greatly expand the number of transporters which we would generate data on and build models such that we could address all the major transporters of interest to drug discovery.