公司原料药立项评估数据

产品立项作为药物研发的第一步，是整个项目的指南针与风向标，其正确性决定了后面所有环节的意义。本数据包数据根据行业专家、一线医生和销售人员等的推荐，采集全球范围内医药行业的产品市场信息、注册信息、专利技术信息以及法规信息，为企业原料药新产品立项提供多维度情报信息支撑；同时借助大数据分析技术，对相关产品进行全面评估和评分，挖掘具有高价值的目标产品，为原料药产品立项提供精准的线索，提升产品立项工作的效能。1.数据采集：根据行业专家、一线医生和销售人员等的推荐，收集产品基本信息；2.数据处理：通过各类商业和官方数据库收集产品的药物类型、全球市场规模及增长趋势、API消耗量及增长趋势、原料药竞争对手、欧美高端市场需求、专利门槛、合成技术难度和法规管制信息等多个特征，并根据多个特征值生成该产品的多个技术指标；3.数据算法：采用机器学习技术中的决策树算法产品进行评分分级，高分产品作为立项优选。打分列通过如下方式计分：属于公司营销领域产品得0.5分，不属于则不得分；市场规模大于等于10亿得1分，1亿~10亿之间得0.5分，小于1亿不得分；市场增长率大于等于15%得0.5分，小于15%不得分；API消耗量大于5吨得0.5分，1吨至5吨之间得0.3分，小于1吨不得分；API消耗量增长率大于等于15%得0.5分，小于15%不得分；竞争对手小于等于5家得0.5分，高于5家不得分；需要欧美药政注册得0.5分，无需注册的不得分；专利过期得0.5分，未过期不得分；合成技术难度低得0.5分，中得0.3分，高不得分；非管制产品得0.5分，管制产品不得分。总分5分，高分产品作为立项优选。

Product project initiation, as the first step in drug research and development (R&D), serves as the compass and weathervane of the entire project, and its correctness determines the validity of all subsequent workflow stages. This dataset collects global pharmaceutical industry product market information, registration information, patent technology information and regulatory information based on recommendations from industry experts, frontline clinicians and sales personnel, providing multi-dimensional intelligence support for the project initiation of new active pharmaceutical ingredient (API) products for enterprises. Meanwhile, leveraging big data analysis technology, it conducts comprehensive evaluation and scoring on relevant products, identifies high-value target products, provides precise clues for API product project initiation, and improves the efficiency of project initiation work.
1. Data Collection: Collect basic product information based on recommendations from industry experts, frontline clinicians and sales personnel.
2. Data Processing: Collect multiple features of products including drug type, global market size and growth trend, API consumption and growth trend, API competitors, demand in high-end European and American markets, patent threshold, synthesis technical difficulty and regulatory control information through various commercial and official databases, and generate multiple technical indicators for the products based on multiple feature values.
3. Data Algorithm: Adopt the decision tree algorithm in machine learning technology to score and grade the products, with high-scoring products being the preferred options for project initiation. The scoring is calculated as follows: 0.5 points will be awarded if the product belongs to the company's marketing field, otherwise no points; 1 point will be awarded if the market size is ≥ 1 billion, 0.5 points if between 100 million and 1 billion, and no points if less than 100 million; 0.5 points will be awarded if the market growth rate is ≥ 15%, otherwise no points; 0.5 points will be awarded if API consumption is > 5 tons, 0.3 points if between 1 ton and 5 tons, and no points if less than 1 ton; 0.5 points will be awarded if API consumption growth rate is ≥ 15%, otherwise no points; 0.5 points will be awarded if the number of competitors is ≤5, otherwise no points; 0.5 points will be awarded if regulatory registration in Europe and America is required, otherwise no points; 0.5 points will be awarded if the patent has expired, otherwise no points; 0.5 points will be awarded if the synthesis technical difficulty is low, 0.3 points if medium, and no points if high; 0.5 points will be awarded if the product is non-regulated, otherwise no points. The total score is 5 points, and high-scoring products are preferred for project initiation.