Model Integration and Configuration for ADC Research

Data Characteristics for Antibody-Drug Conjugate (ADC) Research

ADC research document data originates from preclinical study reports, clinical trial protocols, manufacturing process files, and quality control standards. These documents typically exist in various formats such as PDF, Word, and Excel. Content includes compound structures, pharmacological and toxicological data, preparation procedures, batch analysis reports, and stability studies. Data updates frequently, especially during clinical trials, where data generation is continuous. Document structures are complex, containing extensive specialized terminology, charts, molecular formulas, reaction equations, and units of measurement. For example, pharmacodynamics reports include IC50 and Kd values, toxicology reports involve metrics like LD50 and AUC, and manufacturing process files detail parameters such as reaction temperature (Celsius), pressure (megapascal in Chinese), and concentration (milligrams/Milliliter).

Constraints Imposed by These Characteristics on Model Integration and Configuration

The complexity of ADC research documents places specific demands on model integration and configuration. First, specialized terminology and molecular structures in documents make accurate understanding difficult for general models. This requires incorporating domain-specific models or extensive fine-tuning with domain data. Second, diverse document formats and embedded charts and tables necessitate powerful multimodal parsing capabilities to extract structured information effectively. High-frequency data updates mean models must support incremental learning or periodic retraining to maintain knowledge base timeliness. Additionally, precise numerical values, units, and stoichiometric information in documents require models to strictly differentiate and maintain data integrity during extraction, avoiding errors from unit confusion or numerical truncation. For instance, when processing mg/mL and µg/mL, the model needs to identify and convert units to ensure data consistency.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Research documents often contain many images and charts, leading to large file sizes. A sufficiently large upload limit is necessary.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Complex PDF documents, especially those with numerous formulas and tables, require a longer parsing time.
`Chunk size`	`800–1200 characters`	ADC research document paragraphs are typically long and contain professionally descriptive content with strong contextual relevance. Longer segments help maintain semantic integrity.
`Recall count`	`10 entries`	This ensures enough relevant context is retrieved for complex queries, covering various aspects of research details.
`Similarity threshold`	`0.75`	Domain documents are highly specialized, requiring high similarity to ensure the accuracy of retrieved content.
`maxContext`	`8192 token`	This ensures the model can process long contexts containing many specialized terms and data points, improving understanding.

Common Mistakes

Models produce garbled text or miss critical information when processing documents. This occurs when encoding or model pre-training does not account for ADC domain-specific terminology and special character sets.
Uploading large research reports results in prolonged unresponsiveness or 504 Gateway Timeout errors. This typically indicates PARSE_FILE_TIMEOUT_SECONDS is set too low, not allowing enough time for the model to complete complex document parsing tasks.
Models provide imprecise numerical values or confuse units when answering questions about drug dosage or concentration. This happens when the model has not sufficiently learned the relationship between numbers and units during training, or when unit standardization is not performed during extraction.

How to Verify Configuration

Upload and parse multiple ADC research PDF documents containing molecular structures, experimental data, and charts. Check if parsing results are complete and free of garbled text.
Query specific compound names and key experimental parameters (e.g., IC50 values, LD50 values) from the documents. Verify if the model's answers match the original text, especially for numerical accuracy and units.
Use the FastGPT log system to check for a reduction in PARSE_FILE_TIMEOUT_SECONDS related errors. Confirm that document parsing tasks no longer fail due to timeouts.

The values given are common starting points and should be measured against the reader's own samples.

Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-21.