From 3a145e0da39a00a073086ef53a49f4ff8f7db407 Mon Sep 17 00:00:00 2001
From: ACID Design Lab <82499756+acid-design-lab@users.noreply.github.com>
Date: Fri, 7 Jun 2024 00:28:27 +0300
Subject: [PATCH] Update README.md

---
 README.md | 26 +++++++++++++-------------
 1 file changed, 13 insertions(+), 13 deletions(-)

diff --git a/README.md b/README.md
index f1ca4e5..667466b 100644
--- a/README.md
+++ b/README.md
@@ -18,19 +18,19 @@
 
    Here are the main steps which will allow you to build a precise model for CPP design:
    
-   **1. Data curation and cleaning.** All inappropriate or ambiguous data should be removed or corrected.
-   
-   **2. Data unification.** The data presented in Datasets are heterogeneous and should be unified in terms of variables, measurement units etc.
-   
-   **3. System parametriation.** You need to choose the set of parameters to describe CPPs as well as experimental setup. Most of the models use symbolic representations lacking physico-chemical properties crucial for CPP activity prediction.
-   
-   **4. Model selection.** Best-performing models should be choosen for screening depending on the task complexity (sequence classification or sequence generation).
-   
-   **5. Feature selecction.** After model selection, features used in the model should be choosen showing optimal prediction performance, robustness, and interpretability.
-   
-   **6. Evaluation.** Every model should be evaluated beyond performance on train/test datasets. It can be structural analysis of CPP candidates, modelling of interaction with cellular membranes etc.
-   
-   **7. Project design.** All results should be structured and systematized on GitHub for transparency and reproducibility.
+     **1. Data curation and cleaning.** All inappropriate or ambiguous data should be removed or corrected.
+     
+     **2. Data unification.** The data presented in Datasets are heterogeneous and should be unified in terms of variables, measurement units etc.
+     
+     **3. System parametriation.** You need to choose the set of parameters to describe CPPs as well as experimental setup. Most of the models use symbolic representations lacking physico-chemical properties crucial for CPP activity prediction.
+     
+     **4. Model selection.** Best-performing models should be choosen for screening depending on the task complexity (sequence classification or sequence generation).
+     
+     **5. Feature selecction.** After model selection, features used in the model should be choosen showing optimal prediction performance, robustness, and interpretability.
+     
+     **6. Evaluation.** Every model should be evaluated beyond performance on train/test datasets. It can be structural analysis of CPP candidates, modelling of interaction with cellular membranes etc.
+     
+     **7. Project design.** All results should be structured and systematized on GitHub for transparency and reproducibility.
 
 ### Challenges