HLM_RLM (Human/Rat Liver Microsomal Stability) (doi:10.7910/DVN/ADMGMU)

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Part 1: Document Description
Part 2: Study Description
Part 3: Data Files Description
Part 4: Variable Description
Entire Codebook

Document Description

Citation

Title:

HLM_RLM (Human/Rat Liver Microsomal Stability)

Identification Number:

doi:10.7910/DVN/ADMGMU

Distributor:

Harvard Dataverse

Date of Distribution:

2024-05-21

Version:

1

Bibliographic Citation:

Li, Longqiang, 2024, "HLM_RLM (Human/Rat Liver Microsomal Stability)", https://doi.org/10.7910/DVN/ADMGMU, Harvard Dataverse, V1, UNF:6:dVeTS1rStcjVbjSOJ69VtA== [fileUNF]

Study Description

Citation

Title:

HLM_RLM (Human/Rat Liver Microsomal Stability)

Identification Number:

doi:10.7910/DVN/ADMGMU

Authoring Entity:

Li, Longqiang

Distributor:

Harvard Dataverse

Access Authority:

Park, Haneul

Depositor:

Park, Haneul

Date of Deposit:

2024-05-21

Holdings Information:

https://doi.org/10.7910/DVN/ADMGMU

Study Scope

Keywords:

Chemistry, Medicine, Health and Life Sciences

Abstract:

The HLM_RLM datasets predict the metabolic stability of compounds in Rat and Human Liver Microsomes, which is crucial for early-stage drug development. The datasets include 5,590 compounds for rat liver microsomes and 6,013 for human liver microsomes. Compounds are classified as stable or unstable based on their half-life. Moreover, we have sanitized and organized the datasets, derived from a published paper, into a format with three columns: ID, X, and Y.

Methodology and Processing

Sources Statement

Data Access

Other Study Description Materials

Related Publications

Citation

Title:

Li, L.; Lu, Z.; Liu, G.; Tang, Y.; Li, W. In Silico prediction of human and rat liver microsomal stability via machine learning methods. Chemical Research in Toxicology 2022, 35 (9), 1614–1624. https://doi.org/10.1021/acs.chemrestox.2c00207.

Bibliographic Citation:

Li, L.; Lu, Z.; Liu, G.; Tang, Y.; Li, W. In Silico prediction of human and rat liver microsomal stability via machine learning methods. Chemical Research in Toxicology 2022, 35 (9), 1614–1624. https://doi.org/10.1021/acs.chemrestox.2c00207.

File Description--f10218426

File: hlm.tab

  • Number of cases: 6013

  • No. of variables per record: 3

  • Type of File: text/tab-separated-values

Notes:

UNF:6:YdLqT7vjzdAfYrkuUN2Z7Q==

File Description--f10218425

File: rlm.tab

  • Number of cases: 5592

  • No. of variables per record: 3

  • Type of File: text/tab-separated-values

Notes:

UNF:6:feE/VqlXdX1YpOSAXIKMSA==

Variable Description

List of Variables:

  • ID - ID
  • X - X
  • Y - Y
  • ID - ID
  • X - X
  • Y - Y

Variables

ID

f10218426 Location:

Variable Format: character

Notes: UNF:6:qiUxuJ8YsxsCmHKCZ9ln7A==

X

f10218426 Location:

Variable Format: character

Notes: UNF:6:Yi54Vm32dYJi5ncDoROXuA==

Y

f10218426 Location:

Summary Statistics: Valid 6013.0; StDev 0.4784211092518404; Min. 0.0; Mean 0.3545651089306502; Max. 1.0;

Variable Format: numeric

Notes: UNF:6:jcjOfFLeEMKLZudhrDlN0g==

ID

f10218425 Location:

Variable Format: character

Notes: UNF:6:vzDR40gq8MJ8h9K8wKgnsA==

X

f10218425 Location:

Variable Format: character

Notes: UNF:6:F6lcNz0RdbuoL1XxgImTZg==

Y

f10218425 Location:

Summary Statistics: Valid 5592.0; Mean 0.5924535050071605; Min. 0.0; Max. 1.0; StDev 0.49142195240825504

Variable Format: numeric

Notes: UNF:6:Uwex75nJEiG92dXqL08KVw==