深度学习驱动的药物靶点亲和力预测模型：DeepDTA

需积分: 50 13 浏览量更新于2024-07-16 1 收藏 388KB PDF 举报

本文档"DeepDTA: Deep Drug-Target Binding Affinity Prediction"发表于2018年，是药物发现过程中的一个重要研究方向，主要关注深度学习在预测药物靶标(DTI)相互作用中的应用。传统上，许多计算方法专注于二元分类任务，即判断一个药物-靶点组合是否互动，但这种预测往往忽视了蛋白质与配体之间的真实交互强度，也就是所谓的结合亲和力。结合亲和力的精确预测是一项挑战，因为它是连续的数值范围，而非简单的存在或不存在。研究者提出了一种基于深度学习的模型，该模型仅依赖于目标蛋白和药物的序列信息来预测它们之间的结合亲和力。这与以往的方法有所不同，大部分工作依赖于蛋白质-配体复合物的三维结构或者化合物的二维特征。本研究的独特之处在于，它利用卷积神经网络(CNN)来处理蛋白质序列和化合物的一维表示。CNN的优势在于其在处理序列数据时能够捕捉到局部模式和全局关联，这对于理解和预测蛋白质与小分子之间的相互作用至关重要。该模型的提出是为了克服现有技术的局限性，通过利用大量的DTI数据库中关于结合亲和力的数据，让深度学习模型能够更准确地预测药物与靶点之间的强度关系，从而为新药发现过程提供更精细的信息支持。这篇论文展示了深度学习在DTI预测领域的重要潜力，并为后续的药物设计和优化提供了新的计算工具。

= −log10(

1e9

) (1)

Figure 1A (left panel) illustrates the distribution of the binding affinity values in pK

form.

The peak at pK

value 5 (10000nM) constitutes more than half of the data set (20931 out

of 30056). These values correspond to the negative pairs that either have very weak binding

affinities (K

> 10000nM) or are not observed in the primary screen [47]. As such they are

true negatives.

The distribution of the KIBA scores is depicted in the right panel of Figure 1A. [29]

pre-processed the KIBA scores as f ollows: (i) for each KIBA score, its negative was taken,

(ii) the m inimum value among the negatives was chosen, and (iii) the absolute value of the

minimum was added to all negative scores, thus constructing the final form of the KIBA

scores.

The compound S MIL ES strings of the Davis data set were extracted from the Pubchem

compound database b ased on their Pu bchem CIDs [5]. For KIBA, first the CHEMBL IDs

were converted into Pubchem CIDs and then, the corresponding CIDs were used to extract

the SMILES strings. Figure 1B illustrates the distribution of the lengths of the SMILES

strings of the compounds in the Davis (left) and KIBA (right) data sets. For the compounds

of the Davis dataset, the maximum length of a SMILES is 103, while the average length is

equal to 64. For the comp ou nds of KIBA, the maximum length of a SMILES is 590, while

the average length is equal to 58.

The p rotein sequences of the Davis data set wer e extracted from the UniProt protein

database based on gene names/RefSeq accession numbers [2]. Similarly, the UniProt IDs

of the targets in the KIBA data set were used to collect the protein sequences. Figure 1C

left pan el shows the lengths of the sequences of the proteins in the Davis data set. The

maximum length of a protein sequence is 2549 and the average length is 788 characters.

Figure 1C right panel depicts the distribution of protein sequence length in KIBA targets.

The maximum length of a protein sequence is 4128 and the average length is 728 characters.

We should also note that the Smith-Waterman (S-W) similarity among proteins of the

KIBA data set is at m ost 60% for 99% of the protein pairs. The target similarity is at most

60% for 92% of the protein pairs for the Davis data set. These statistics indicate that both

data sets are non-redundant.

Input Representation

We used integer/label encoding that uses integers for the categories to represent inputs. We

scanned approximately 2M SMILES sequences th at we collected from Pubchem and com-

piled 64 labels (unique letters). For protein sequences, we scanned 550K protein sequences

from UniProt and extracted 25 categories (unique letters).

Here we represent each label with a corresponding integer (e.g. “C”:1, “H”:2, ‘N”:3 etc.).

The label encoding for the example S MIL ES, “CN=C=O”, is given below.



C N = C = O





1 3 63 1 63 5



Protein sequences are encoded in a sim ilar way using label encodings. Both SMILES and

protein sequences have varying lengths. Hence, in order to create an effective representation

剩余17页未读，继续阅读

温馨娜

粉丝: 43
资源: 1

深度学习驱动的药物靶点亲和力预测模型：DeepDTA

binding-affinity-prediction

visual prediction中的几个metrics

化合物评价函数2020年之后的文献

node-sass-windows-x64-93-binding.node

ubuntu安装WARP

error while loading shared libraries: libxcb-render-util.so.0

解决mybatis-plus的org.apache.ibatis.binding.BindingException Create breakpoint Invalid bound statement (not found)

最新资源