Units
1. Introduction to Artificial Intelligence in Chemistry: Roles, Boundaries, Validation and Ethics 2. Molecular Representation and Chemical Structure: Validation with SMILES, InChI and RDKit 3. Reaction Prediction and Mechanism: Product, Condition and Adverse Reaction Prediction 4. Spectrum Interpretation Support: NMR, IR and Mass Spectrometry 5. Literature Review and Synthesis Planning: Source, Procedure and Roadmap 6. Chemical Data Analysis and Python: pandas, Stoichiometry and Calibration 7. Molecular Property Prediction and QSAR: Resolution, pKa and Model Limits 8. Laboratory Documentation: Experiment Notebook, ELN and Reproducibility 9. Safety and Hazard Assessment: GHS, SDS and the Limits of Artificial Intelligence 10. Confirmation, Hallucination, and Scientific Integrity 11. Human in Experimentation: Ethics, Privacy, Responsibility and End-to-End Workflow
Unit 5 / 11

Literature Review and Synthesis Planning: Source, Procedure and Roadmap

Gains:

  • Ability to position artificial intelligence as a dangerous tool in the production of powerful, concrete citations in concepts, abstracts and search strategy
  • Ability to eliminate the risk of fake sources by verifying each citation and DOI in the database
  • Ability to establish a secure workflow in which humans find the real article and summarize the text to artificial intelligence

Literature Review and Synthesis Planning: Source, Procedure and Roadmap with AI

Every chemistry project begins with a literature review: "Has this compound been made before? By what method? With what yield?" AI is a great accelerator at this stage; summarizes the topic, explains key concepts, generates search terms, outlines procedures. But it is also the source of the most dangerous error in chemistry: spurious attribution. AI can make up articles, authors, and DOIs that look realistic but don't exist at all. In this unit, we will learn to use AI in literature and synthesis planning, but without abandoning the discipline of citation.

Why is citation discipline critical?

A language model produces text; does not query the database (unless stated otherwise). “Journal of Organic Chemistry, 2018, 83, 4521, Zhang et al.” A resource of the form is just "a set of words and numbers that seem possible" for the model. This source may or may not be real; the model does not distinguish between the two. Therefore, no citation given by YZ is used without being confirmed in a database (DOI analyzer, PubMed, Crossref, publisher site).

Attention: Just because the AI ​​gives a DOI does not mean that that DOI is valid. Resolve DOI via https://doi.org/; If it doesn't lead to an article, that source doesn't exist, even if the title and author seem authentic.

Where AI is strong and weak in the literature

  • Powerful: Explaining a concept, outlining a class of reactions, generating search keywords, outlining the general steps of a procedure, summarizing the text of an article (when you paste it).
  • Weak: Giving "from memory" the existence of a specific article, exact page/volume/DOI information, exact yield value. These are the areas most open to fabrication.

This distinction gives rise to the following strategy: use AI as a text processing tool (summarization, classification), not as a source of truth. You find the real article, have AI read it and summarize it.

Step by step: safe literature + planning flow

  1. Learn the concept: Have the AI explain the topic, reaction class, terms. The risk of fabrication is low here.
  2. Generate search term: Ask AI for 8-10 keywords/synonyms and possible journals; Search the real database with these.
  3. Get the real article: Find the article yourself from PubChem/Reaxys/Google Scholar.
  4. Summarize: Paste the article text into AI and make a structured summary (method, condition, yield, limitation).
  5. Make a plan: Create a synthesis roadmap for a goal; connect each rung to the actual source.
  6. Verify: Resolve each citation with DOI; Compare each critical number with the source text.

Four copyable templates

1) Concept + search term generation (safe area):

Topic: Establishing an aryl-aryl bond via Suzuki-Miyaura coupling reaction.Task:1) Explain the reaction in 5 sentences (define terms).2) Generate 10 keywords/synonyms for literature search.3) Recommend 5 journals where this topic is frequently published.Rule: DO NOT GIVE specific article/DOI; I will search for them in the database.

2) Article summarization (you provide the text):

Below I am pasting the experimental part of an article.[TEXT]Task: Summarize with the following headings:- Target compound and its significance- Method/reagents used- Conditions (temperature, time, solvent)- Yield- Limitations statedDo not add any numbers NOT in the text; otherwise write "unspecified".

3) Synthesis roadmap (depending on source):

Target: 2-phenylbenzimidazole (SMILES: c1ccc(cc1)c1nc2ccccc2[nH]1).Task: Give a draft of a 2-step synthesis plan.For each step: reagents, condition, purification method.Rule: For each step, add "typical reference of this type of reaction with which keyword to search for" information; DOI FAKE.

4) Citation confirmation checklist:

There are 4 references below. Produce a fact-checking checklist for each: which fields (DOI, journal, volume, year, author) should I verify and where?[CITATIONS]Note: You DO NOT claim that the citations are correct; Just tell me step by step how to confirm.

Weak prompt / Strong prompt

Weak:

Give me 5 sources on the synthesis of this compound.

The AI ​​likely produces realistic but unverified attributions, some of which are fabricated.

Strong:

Topic: Synthesis of compound I will find the real sources from the database; You strengthen my search strategy.

The difference: We directed the AI ​​towards work in which it is strong (search strategy, concept) and kept it away from work that is prone to fabrication (concrete attribution).

Source types and reliability

Source/tool

What provides

reliability

Role

AI (language model) from memory

Concept, outline, search idea

High on concept, low on reference

idea/summary

AI + pasted text

Summary of the given text

High (text dependent)

summarizing

DOI parser (doi.org)

Authenticity of the attribution

high

Confirmation

PubChem / Reaxys / SciFinder

compound, reaction, source

high

primary search

Google Scholar / Crossref

Article finding, citation

high

verification

mini cases

Case 1 — Three fabricated sources. A researcher asked AI for 6 sources on a topic. When he solved the DOIs one by one, 3 of them were invalid; The headlines were convincing, but the articles were not. The verification took 25 minutes but prevented 3 fake citations from entering the article. Lesson: each reference is solved one by one.

Case 2 — False yield. “Efficiency is 92%,” AI said when outlining a procedure. When the user found and looked at the original article, the yield was 64%; The AI ​​had made up the number from memory. Lesson: compare critical numbers with source text, don't trust the AI ​​summary.

Case 3 — Value in concept, risk in reference. A student had the AI ​​explain a reaction class; The explanation was accurate and instructive. Then “top 3 articles?” he said; Two of them were fabrications. AI was both very useful and dangerous in the same session. Lesson: the same vehicle shows different reliability in different tasks; Trust according to your duty.

Common mistakes

  • Using references without confirmation. The most common and most serious mistake; may lead to article retraction.
  • Getting yield/condition numbers from the summary. The AI ​​summary sometimes reflects the “possible value” rather than the source; compare with original.
  • Mistaking AI for a search engine. Unless stated otherwise, the language model does not query the live database; produces from memory.
  • Searching in one language. Simply leaving key terms out of Turkish/English is missing resources; AI aids in synonym generation.
  • Confusing concept with source. The fact that AI explains the correct concept does not indicate that the reference it gives is correct.
Tip: The most robust workflow is “human first finds, then AI summarizes.” You bring the real article and have the AI ​​read the text. So you use the AI's strength (summarization) and disable its weakness (source fitting).

In summary

  • AI is dangerous in producing strong, concrete citations in concepts, summaries, and search strategies in the literature.
  • False citation (fabricated article/DOI) is the most serious AI risk in chemistry; Every reference is confirmed.
  • The safest flow: human finds the real article, AI summarizes the text.
  • Take critical numbers, such as yield and condition, from the source text, not the abstract.
  • Connect each step in the synthesis plan to a real source.

Application task

Select a target compound. First, ask the AI ​​for search terms and a conceptual summary only (without asking for concrete attribution). Find at least two articles in a real database with these terms. Summarize article texts to AI and compare yield/condition numbers to the original. Separately, purposely ask the AI ​​for “5 sources” and decipher the DOIs it gives one by one: how many were real? Write a one-page "citation verification report".

checklist

  • [ ] I position AI as a concept/summary tool, not a search engine.
  • [ ] When I want concrete citation, I decode each DOI from doi.org.
  • [ ] I'm comparing yield/condition numbers to the original source.
  • [ ] I find the real article and have the AI ​​summarize the text.
  • [ ] I attribute each step in the synthesis plan to a source.
  • [ ] I have prepared at least one citation verification report.
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