{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/8"}],"enrichment":{"capability":"Converts between SELFIES (Self-Referencing Embedded Strings) and SMILES molecular representations, ensuring syntactically and semantically valid molecular strings for machine learning applications.","skillfed_tags":["chemistry","molecular-ml","graph-representation"],"use_cases":["Training generative models (VAEs, GANs) on molecular data where every sampled string must decode to a valid molecule","Converting existing SMILES datasets to SELFIES for more robust chemical exploration and mutation","Encoding molecules as fixed-length vectors or integer sequences for neural network input","Creating random valid molecules for high-throughput virtual screening or lead generation","Analyzing molecular structure by tokenizing and attributing SELFIES symbols to SMILES output tokens"],"what_it_does":"SELFIES is a molecular string representation designed to guarantee that every string encodes a valid, semantically meaningful molecule. Unlike SMILES, which can produce invalid molecules through random mutations, SELFIES enforces chemical constraints at the string level, making it particularly useful for generative machine learning models that need to explore molecular space without producing chemically impossible structures.\n\nThe package provides bidirectional translation between SELFIES and SMILES formats, tokenization, encoding/decoding for neural networks, and customizable semantic constraints (including hypervalent chemistry). It has no external runtime dependencies and supports Python 3.7 and later, making it straightforward to integrate into existing chemistry or ML pipelines.","worth_installing":"Yes, if you work with generative chemistry models or need guaranteed-valid molecular representations. The package is well-established (first released 2019, now at 2.2.0) with no known vulnerabilities and permissive licensing. The aging maintenance status (576 days since last release) is not a blocker for stable use, but verify that it meets your specific ML framework and performance requirements before committing to a production pipeline."},"id":"selfies","links":{"html":"https://skillfed.io/packages/selfies","md":"https://skillfed.io/packages/selfies.md","pypi":"https://pypi.org/project/selfies/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2025-01-15","license_spdx":null,"license_treatment":"permissive","name":"selfies","python_support":"supports_current","summary":"SELFIES (SELF-referencIng Embedded Strings) is a general-purpose, sequence-based, robust representation of semantically constrained graphs."},"popularity":{"monthly_downloads":181097,"position":10134,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.2.0"}