Invited Perspective: Screening in Academe: A Perspective on Implementation of University-Based Small Molecule Screening
Notice bibliographique
Résumé
With the emergence ofsmall molecule screening as a power ful research tool in biology, biomolecular screening has arrived in the laboratories ofacademe. More and more, researchers in universities and hospital research institutes are recognizing the power ofsmall molecules as probes ofbiochemical and biological systems. At McMaster University in Hamilton, Canada, we have established a state-of-the-art small molecule screening laboratory that became fully operational a little more than a year ago (http:// hts.mcmaster.ca). It is with considerable enthusiasm, as the founding director of that laboratory, that I offer the following perspectives on screening in an academic setting and on my own experiences in setting up such a facility. Since the establishment ofthe first academic screening operations, including, for example, that of the Harvard Institute of Chemistry and Cell Biology in the late 1990s, there has been a groundswell ofinterest among academic researchers in highthroughput screening (HTS). Today, biological researchers in most research-intensive academic institutions are, at the very least, considering the establishment ofcapabilities and a presence in small molecule screening to fuel research activities in the emerging field ofchemical biology. Nevertheless, the implementation ofa reason ably capable screening facility with its associated liquid-handling, instrumentation, compound, and information management systems is a complex, costly, and energetic undertaking. In setting up a state-of-the-art screening laboratory, it has been instructive for me to reflect on how advances in HTS in the private sector have exerted recent influence on research directions in bio logical research at universities and research institutes. This trend contrasts with the conventional academic view ofinnovation that has emerging technology moving from university to industry. Advances in biological research have, ofcourse, been among the most celebrated university-based innovations. The once purely academic pursuits ofbiological chemistry and cell biology have slowly but firmly established themselves in the research paradigm off ormerly chemistry-centered pharma, beginning with revolu tionary advances in molecular biology in the late 1970s. It is per haps ironic, ifnot remarkable, then, that similarly revolutionary progress made by high-throughput screeners in the pharmaceutical sector has affected the technology now available to chemical biologists in academe. Equipped with robust robotics, information systems, and wellestablished screening methodologies, all developed by and large in the biotechnology and pharmaceutical sector, biochemists and cell biologists in academe are turning in earnest to small molecule screening as a fresh approach to discovering molecular probes of systems under their study. With the freedom to innovate and publish, academic screeners will now surely be in a position to return the favor to their private-sector colleagues with new discoveries and new approaches. It is clearly fitting that the Society for Biomolecular Screening (SBS), founded by private-sector researchers, has taken steps to welcome screeners from academe with the formation of the Academic Outreach Committee in the spring of2003. The committee is composed ofa mix ofindividuals from industry and academe, including myself, who have a keen interest in building bridges among academic screeners and their colleagues in pharmaceutical, biotechnology, and instrumentation companies. The committee also has the mandate offurthering the interests and profile of academic screeners in the SBS and has the potential to function as a nucleus for the exchange of ideas and experiences among academic screeners. Our own HTS lab was founded by 3 principal investigators at McMaster whose research interests include antimicrobial research (Gerry Wright and I) and materials science (John Brennan). The genesis and funding for the laboratory came as part of a provincewide initiative in genomics and included close partnerships with combinatorial chemistry laboratories at York University and the University ofToronto. The thinking was that the chemists and screeners could work together, for example, in lead optimization activities. Indeed, the practicality ofattracting the interest of collaborators in synthetic chemistry for downstream optimization of hits from commercial libraries is frequently a concern among
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,016 | 0,023 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,006 | 0,003 |
| Communication savante | 0,010 | 0,006 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,053 | 0,028 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,024 | 0,005 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».