2022
DOI: 10.1208/s12248-021-00644-3
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Machine Learning and Artificial Intelligence in Pharmaceutical Research and Development: a Review

Abstract: Graphical abstract Over the past decade, artificial intelligence (AI) and machine learning (ML) have become the breakthrough technology most anticipated to have a transformative effect on pharmaceutical research and development (R&D). This is partially driven by revolutionary advances in computational technology and the parallel dissipation of previous constraints to the collection/processing of large volumes of data. Meanwhile, the cost of bringing new drugs to market and to patients has become pro… Show more

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Cited by 130 publications
(66 citation statements)
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“…Chemistry is an empirical science, and high-throughput experimentation (HTE) has the potential to grow into a pervasive enabling technology. In areas adjacent to biology (e.g., biochemistry and pharmaceuticals), HTE tools and methods already represent the gold standard for quantitative structure–activity relationship (QSAR) studies due to a favorable mix of scientific, technological, and market aspects. For instance, biological activity is a single property that can be measured with a combinatorial approach under the mild conditions at which biochemical processes typically occur, the commercial relevance of rapid automated assays is huge, and last but not least system complexity calls for statistical models in which large HTE databases are analyzed by means of advanced applications of artificial intelligence (AI) such as machine learning (ML). …”
Section: Introductionmentioning
confidence: 99%
“…Chemistry is an empirical science, and high-throughput experimentation (HTE) has the potential to grow into a pervasive enabling technology. In areas adjacent to biology (e.g., biochemistry and pharmaceuticals), HTE tools and methods already represent the gold standard for quantitative structure–activity relationship (QSAR) studies due to a favorable mix of scientific, technological, and market aspects. For instance, biological activity is a single property that can be measured with a combinatorial approach under the mild conditions at which biochemical processes typically occur, the commercial relevance of rapid automated assays is huge, and last but not least system complexity calls for statistical models in which large HTE databases are analyzed by means of advanced applications of artificial intelligence (AI) such as machine learning (ML). …”
Section: Introductionmentioning
confidence: 99%
“…In healthcare, ML-powered products are increasingly receiving regulatory clearance, with a 2020 study finding that AI/ML systems are winning approval from the US Food and Drug Administration (FDA) at an accelerating rate ( Benjamens et al, 2020 ; Rajpurkar et al, 2022 ). The transformative effect that ML has had on other industries has prompted the pharmaceutical industry to identify opportunities to re-invent traditional time-consuming processes in bringing medicines into market ( Abramov et al, 2022 ; Elbadawi et al, 2021a ; Kolluri et al, 2022 ; Lou et al, 2021 ; Paul et al, 2021 ; Thomas et al, 2021 ; Yang et al, 2019 ).…”
Section: Introductionmentioning
confidence: 99%
“…Perkembangan Artificial Neural Network (ANN) sangat memungkinkan untuk melakukan prediksi [1]. Dari data yang ada pada sistem informasi [2], bisa dilakukan pengolahan sehingga menghasilkan informasi dan pengetahuan menggunakan Machine Learning atau pembelajaran mesin.…”
Section: Pendahuluanunclassified
“…Pada saat melakukan konfigurasi ANN [20] [22]. Pada Puskesmas yang sama, [23], [1] melakukan prediksi kebutuhan obat menggunakan metode Learning Vector Quatization. Hasil prediksinya menunjukkan akurasi yang tidak jauh lebih baik dari Backpropagation yaitu 78.57%.…”
Section: Pendahuluanunclassified