Checklist NTO AI 2024 Stage 2 (qualifying) · IV.1.1 task
Lipophilicity Prediction
Russian title: Предсказание липофильности
Predict the lipophilicity of small organic molecules from their SMILES strings.
The task
Participants must develop an algorithm that predicts the lipophilicity of small organic molecules, essentially the octanol-1/water partition coefficient, an important parameter in drug development. The training table gives SMILES strings with dimensionless lipophilicity values (for example, 1.19); at test time the code processes the same kind of table without the answer column.
Abridged and translated by SOTA from the official Russian materials. The official statement has the exact rules, and it wins wherever this summary differs.
In English
This task was published in Russian. SOTA translated it into English on 16 September 2026.
Read the task statement in English
Lipophilicity Prediction
English translation by SOTA – AI Community of the Russian original. Organisers who would like this translation removed can email [email protected].
Source: the 2023/24 task collection of the National Technology Olympiad (NTO), "Artificial Intelligence" profile, Stage 2, pp. 88–89: original PDF.
Second qualifying stage
The tasks of the second qualifying stage focus on property prediction and the design of small organic molecules. These tasks are highly relevant to modern chemistry and biology, as they play a key role in the development of new drugs, materials and technological processes.
Individual task
Task IV.1.1. (100 points)
Topics: data analysis, QSAR methods, chemical databases.
Problem statement
Participants are required to develop an algorithm capable of predicting the lipophilicity of small organic molecules. In essence, this parameter is the partition coefficient between a non-polar and a polar phase, that is, the ratio of the solubility of a compound in 1-octanol to its solubility in water. Lipophilicity is an important parameter, and its calculation is an integral part of the development of new medicinal drugs.
Input format
A file containing the string representation (Simplified Molecular Input Line Entry System, SMILES).
Output format
A file with the CSV extension containing the predicted lipophilicity values of the molecules (each value on a new line).
Example of input data (file fragment)
CCOc1ccccc1O
Cc1cccc(C)n1
CC(C)C#N
Program output (file fragment)
1.73
1.44
0.23
Assessment criteria
Solutions will be assessed with the standard metric for regression tasks, the root mean square error (RMSE).
Link to the description of the metric: https://scikit-learn.org/stable/modules/model_evaluation.html#mean-squared-error.
Solution
Participants have access to a baseline solution from the task developers that uses the scikit-learn and RDKit libraries.
The intended pipeline of the model includes:
- generating a representation of the molecules that is suitable for use as input data for predictive models;
- predicting the target parameter from the generated representation.
An example solution (with comments) is available at: https://disk.yandex.ru/d/unyk1lr18_zxiw.
Translated by SOTA. The Russian original is the official version and wins wherever the two differ. The statement is task IV.1.1 (individual task of the second qualifying stage) on pages 88–89 of the NTO 2023/24 task collection for the Artificial Intelligence profile. The example solution it links to on Yandex Disk was not translated. If you organise this olympiad and would like the translation removed, email [email protected] and we will take it down.
At a glance
- You get
- A file of SMILES strings (for example CCOc1ccccc1O).
- You submit
- A CSV file with one predicted lipophilicity value per line.
- Scoring
- RMSE (root mean squared error).
- Rules
- Individual task.
- Format
- Stage 2, individual task (100 points).