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However, the applicability of these compounds is often challenged by synthetic viability and cost-effectiveness. Researchers introduced proxy-scores, known as synthethic accessiblity scoring, to quantify the ease of synthesis for virtual molecules. Despite their utility, existing synthetic accessibility tools have notable limitations: they overlook compound purchasability, lack physical interpretability, and often rely on imperfect computer-aided synthesis planning algorithms. We introduce\n                    <jats:italic>MolPrice<\/jats:italic>\n                    , an accurate and fast model for molecular price prediction. Utilizing self-supervised contrastive learning,\n                    <jats:italic>MolPrice<\/jats:italic>\n                    autonomously generates price labels for synthetically complex molecules, enabling the model to generalize to molecules beyond the training distribution. Our results show that\n                    <jats:italic>MolPrice<\/jats:italic>\n                    reliably assigns higher prices to synthetically complex molecules than to readily purchasable ones, effectively distinguishing different levels of synthetic accessibility. Furthermore,\n                    <jats:italic>MolPrice<\/jats:italic>\n                    achieves competitive performance on literature benchmarks for synthetic accessibility. To demonstrate its practical utility, we conduct a virtual screening case study, illustrating how\n                    <jats:italic>MolPrice<\/jats:italic>\n                    successfully identifies purchasable molecules from a large candidate library.\n                    <jats:italic>MolPrice<\/jats:italic>\n                    bridges the gap between generative molecular design and real-world feasibility by integrating cost-awareness into synthetic accessibility assessment, making it a powerful model to accelerate molecular discovery.\n                  <\/jats:p>","DOI":"10.1186\/s13321-025-01076-3","type":"journal-article","created":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T17:13:30Z","timestamp":1759166010000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MolPrice: assessing synthetic accessibility of molecules based on market value"],"prefix":"10.1186","volume":"17","author":[{"given":"Friedrich","family":"Hastedt","sequence":"first","affiliation":[]},{"given":"Klaus","family":"Hellgardt","sequence":"additional","affiliation":[]},{"given":"Sophia","family":"Yaliraki","sequence":"additional","affiliation":[]},{"given":"Dongda","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Antonio","family":"del Rio Chanona","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,9,29]]},"reference":[{"key":"1076_CR1","doi-asserted-by":"publisher","first-page":"589","DOI":"10.1038\/s42256-024-00843-5","volume":"6","author":"Y Du","year":"2024","unstructured":"Du Y, Jamasb AR, Guo J, Fu T, Harris C, Wang Y, Duan C, Li\u00f2 P, Schwaller P, Blundell TL (2024) Machine learning-aided generative molecular design. 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