Machine learning models for drug–target interactions: current knowledge and future directions

D’Souza, Sofia; Prema, K. V.; Balaji, S.

doi:10.1016/j.drudis.2020.03.003

Cited by 151 publications

(82 citation statements)

References 64 publications

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“…The process of developing a drug can take about 12 years or more from development to approval for launching the drug on the market [54]. According to Shen et al, the traditional drug discovery process is largely based on high throughput screening (HTS), which is an acceptable performance technique but of high cost and low efficiency [55].…”

Section: Methodsmentioning

confidence: 99%

Chagas Disease: Perspectives on the Past and Present and Challenges in Drug Discovery

et al. 2020

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Chagas disease still has no effective treatment option for all of its phases despite being discovered more than 100 years ago. The development of commercial drugs has been stagnating since the 1960s, a fact that sheds light on the question of how drug discovery research has progressed and taken advantage of technological advances. Could it be that technological advances have not yet been sufficient to resolve this issue or is there a lack of protocol, validation and standardization of the data generated by different research teams? This work presents an overview of commercial drugs and those that have been evaluated in studies and clinical trials so far. A brief review is made of recent target-based and phenotypic studies based on the search for molecules with anti-Trypanosoma cruzi action. It also discusses how proteochemometric (PCM) modeling and microcrystal electron diffraction (MicroED) can help in the case of the lack of a 3D protein structure; more specifically, Trypanosoma cruzi carbonic anhydrase.

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Section: Methodsmentioning

confidence: 99%

Chagas Disease: Perspectives on the Past and Present and Challenges in Drug Discovery

et al. 2020

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“…In the identification of the novel targets of drugs, there has been increasing interest in predicting drug–target interaction (DTI), given its relevance for side effect prediction and drug‐repositioning attempts 101 . The availability of heterogeneous biological data on known DTI has enabled the development of various AI/ML‐based strategies to exploit unknown DTI, 102 including ensemble learning, 103–106 tree‐ensemble learning, 107 active learning, 108 DL, 109 end‐to‐end DL, 110 and kernel‐based learning 111–115 .…”

Section: Ai/ml Applications In Drug Discoverymentioning

confidence: 99%

Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions

Vatansever

Schlessinger

Wacker

et al. 2020

Medicinal Research Reviews

216

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Neurological disorders significantly outnumber diseases in other therapeutic areas. However, developing drugs for central nervous system (CNS) disorders remains the most challenging area in drug discovery, accompanied with the long timelines and high attrition rates. With the rapid growth of biomedical data enabled by advanced experimental technologies, artificial intelligence (AI) and machine learning (ML) have emerged as an indispensable tool to draw meaningful insights and improve decision making in drug discovery. Thanks to the advancements in AI and ML algorithms, now the AI/ML‐driven solutions have an unprecedented potential to accelerate the process of CNS drug discovery with better success rate. In this review, we comprehensively summarize AI/ML‐powered pharmaceutical discovery efforts and their implementations in the CNS area. After introducing the AI/ML models as well as the conceptualization and data preparation, we outline the applications of AI/ML technologies to several key procedures in drug discovery, including target identification, compound screening, hit/lead generation and optimization, drug response and synergy prediction, de novo drug design, and drug repurposing. We review the current state‐of‐the‐art of AI/ML‐guided CNS drug discovery, focusing on blood–brain barrier permeability prediction and implementation into therapeutic discovery for neurological diseases. Finally, we discuss the major challenges and limitations of current approaches and possible future directions that may provide resolutions to these difficulties.

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“…Denetimsiz öğrenme (unsupervised learning), verilerle ilişki bulmak için etiketli verilerle eğitime bağlı değildir. Veri odaklı olup, gizli yapıları bulmak için verilerdeki kalıpları öğrenmeyi amaçlar [25].…”

Section: Yapay Zekâ'nın Alt Gruplarıunclassified

İlaç Keşfi̇ Ve Geli̇şti̇ri̇lmesi̇nde Yapay Zekâ

Çelik¹,

Arslan²,

Tunç³

et al. 2021

Ankara Universitesi Eczacilik Fakultesi Dergisi

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Amaç: Makine zekâsı olarak da bilinen Yapay Zekâ'nın ilaç keşfi ve geliştirilme sürecindeki yeri ve öneminin ortaya konması amaçlanmıştır.Sonuç ve Tartışma: İlaç keşfi ve geliştirme aşamaları, insan sağlığına ve refahına katkıda bulunan en önemli çeviri bilim etkinlikleri arasındadır. Bununla birlikte, yeni bir ilacın geliştirilmesi oldukça karmaşık, pahalı ve oldukça uzun bir süreçtir. Maliyetlerin nasıl azaltılacağı ve yeni ilaç keşfinin nasıl hızlandırılacağı endüstride zorlu ve ivedi ile çözülmesi gereken bir soru haline gelmiştir. Yapay zekânın (AI) yeni deneysel teknolojilerle bir araya gelmesi, yeni ilaç arayışını daha hızlı, daha ucuz ve daha etkili hale getirmesi beklenmektedir. Bu derlemede, ilaç keşif sürecini hızlandırmak için ortaya çıkan yapay zekâ uygulamaları ele alınmıştır.

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Machine learning models for drug–target interactions: current knowledge and future directions

Cited by 151 publications

References 64 publications

Chagas Disease: Perspectives on the Past and Present and Challenges in Drug Discovery

Chagas Disease: Perspectives on the Past and Present and Challenges in Drug Discovery

Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions

İlaç Keşfi̇ Ve Geli̇şti̇ri̇lmesi̇nde Yapay Zekâ

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