Title: Deep Learning for Drug Design: an Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era

Authors: Yankang Jing, Yuemin Bian, Ziheng Hu, Lirong Wang, Xiang‐Qun Xie

Publishing date: 30-Mar-2018

Article Expected CPY (Citations per Year): 3.54 Expected CPY Help

Article Actual CPY: 40.00 Actual CPY Help

Article Co-Citation FCR (Field Citation Rate): 4.22 FCR Help

Article Co-Citation Network Size: 20809 Co-Citation Network Size Help

Article Topics: Computational Methods in Drug Discovery, Accelerating Materials Innovation through Informatics, Advances in Metabolomics Research Topics help

Article Keywords: Drug Target Identification, Network Pharmacology, Materials Discovery Keywords Help

Journal: ˜The œAAPS journal

Journal IF-ycr: 3.118 Journal IF-ycr Help

Journal short code: NA

Journal ISSN: 1550-7416

Journal OA-ID: 74983066

Expected CPY Help: Predicted citations per year for this article, derived from its Field Citation Rate (FCR) using a benchmark regression of NIH-funded papers. Values above actual CPY indicate under-performance; below indicate over-performance. Used as the denominator of the Relative Citation Ratio.

Actual CPY Help: Average yearly citations the article has received from publication through the current year, adjusted for partial years. Used as the numerator of the Relative Citation Ratio.

FCR Help: Mean journal citation rate for all papers in the article's co-citation network. For each network paper we substitute its journal’s impact factor (calculated from open data) as a proxy for citations per year, then average these values. This captures the citation intensity of the article's immediate research field and forms the basis for computing expected CPY.

Co-Citation Network Size Help: Number of unique papers co-cited with this article by its citing papers; larger networks yield more stable field estimates when calculating FCR and expected CPY.

Topics Help: OpenAlex assigns topics to each paper with an AI model that considers the title, abstract, journal, and citation links. Tags are chosen from about 4,500 research areas, and the highest-confidence tag becomes the paper's primary topic. Every topic sits in a hierarchy of domain, field, and subfield, so you can see exactly where the work fits in the wider map of science.

Keywords Help: Keywords are generated automatically from the paper's assigned topics. The OpenAlex system selects candidate terms, then keeps up to five that match closely with the title or abstract. These keywords highlight specific concepts or methods and give a quick complement to the broader topic tags.

Journal IF-ycr Help: Journal IF-ycr is a two-year impact factor recalculated from OpenAlex's open citation data. For a given journal and year Y, we:

1. Count citations made in year Y by any paper to items that the journal published in years Y-1 and Y-2 (excluding the current year).
2. Divide that citation count by the number of articles the journal published in those same two years.