[
  {
    "id": "J48PG2E9",
    "type": "article-journal",
    "abstract": "In the past few years, there has been considerable activity in both academic and industrial research to develop innovative machine learning approaches to locate novel, high-performing molecules in chemical space. Here we describe a new and fundamentally different type of approach that provides a holistic overview of how high-performing molecules are distributed throughout a search space. Based on an open-source, graph-based implementation [J. H. Jensen, Chem. Sci., 2019, 10, 3567–3572] of a traditional genetic algorithm for molecular optimisation, and influenced by state-of-the-art concepts from soft robot design [J. B. Mouret and J. Clune, Proceedings of the Artificial Life Conference, 2012, pp. 593–594], we provide an algorithm that (i) produces a large diversity of high-performing, yet qualitatively different molecules, (ii) illuminates the distribution of optimal solutions, and (iii) improves search efficiency compared to both machine learning and traditional genetic algorithm approaches.",
    "container-title": "Chemical Science",
    "DOI": "10.1039/D0SC03544K",
    "ISSN": "2041-6539",
    "issue": "42",
    "journalAbbreviation": "Chem. Sci.",
    "language": "en",
    "page": "11485-11491",
    "source": "pubs.rsc.org",
    "title": "Illuminating elite patches of chemical space",
    "URL": "https://pubs.rsc.org/en/content/articlelanding/2020/sc/d0sc03544k",
    "volume": "11",
    "author": [
      {
        "family": "Verhellen",
        "given": "Jonas"
      },
      {
        "family": "Abeele",
        "given": "Jeriek Van",
        "dropping-particle": "den"
      }
    ],
    "accessed": {
      "date-parts": [
        [
          "2024",
          11,
          22
        ]
      ]
    },
    "issued": {
      "date-parts": [
        [
          "2020",
          11,
          4
        ]
      ]
    }
  }
]