Artificial intelligence in early drug discovery enabling precision medicine

Published on Jun 2, 2021in Expert Opinion on Drug Discovery4.887
· DOI :10.1080/17460441.2021.1918096
Fabio Boniolo (TUM: Technische Universität München), Emilio Dorigatti2
Estimated H-index: 2
(LMU: Ludwig Maximilian University of Munich)
+ 3 AuthorsMichael P Menden
Introduction: Precision medicine is the concept of treating diseases based on environmental factors, lifestyles, and molecular profiles of patients. This approach has been found to increase success rates of clinical trials and accelerate drug approvals. However, current precision medicine applications in early drug discovery use only a handful of molecular biomarkers to make decisions, whilst clinics gear up to capture the full molecular landscape of patients in the near future. This deep multi-omics characterization demands new analysis strategies to identify appropriate treatment regimens, which we envision will be pioneered by artificial intelligence.Areas covered: In this review, the authors discuss the current state of drug discovery in precision medicine and present our vision of how artificial intelligence will impact biomarker discovery and drug design.Expert opinion: Precision medicine is expected to revolutionize modern medicine; however, its traditional form is focusing on a few biomarkers, thus not equipped to leverage the full power of molecular landscapes. For learning how the development of drugs can be tailored to the heterogeneity of patients across their molecular profiles, artificial intelligence algorithms are the next frontier in precision medicine and will enable a fully personalized approach in drug design, and thus ultimately impacting clinical practice.
📖 Papers frequently viewed together
1 Citations
3 Citations
1 Citations
#1Deisy Morselli Gysi (NU: Northeastern University)H-Index: 8
#2Italo Faria do Valle (NU: Northeastern University)H-Index: 13
Last. Albert-László Barabási (NU: Northeastern University)H-Index: 154
view all 11 authors...
The COVID-19 pandemic has highlighted the need to quickly and reliably prioritize clinically approved compounds for their potential effectiveness for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. Here, we deployed algorithms relying on artificial intelligence, network diffusion, and network proximity, tasking each of them to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. To test the predictions, we used as ground truth 918 drugs experimentally screene...
16 CitationsSource
#1Jung-Eun Shin (Harvard University)H-Index: 4
#2Adam J. Riesselman (Harvard University)H-Index: 10
Last. Debora S. Marks (Harvard University)H-Index: 48
view all 10 authors...
Abstract The ability to design functional sequences and predict effects of variation is central to protein engineering and biotherapeutics. State-of-art computational methods rely on models that leverage evolutionary information but are inadequate for important applications where multiple sequence alignments are not robust. Such applications include the prediction of variant effects of indels, disordered proteins, and the design of proteins such as antibodies due to the highly variable complemen...
19 CitationsSource
#1Zikun Yang (SC: University of Southern California)H-Index: 1
#2Paul Bogdan (SC: University of Southern California)H-Index: 28
Last. Shahin Nazarian (SC: University of Southern California)H-Index: 17
view all 3 authors...
The rampant spread of COVID-19, an infectious disease caused by SARS-CoV-2, all over the world has led to over millions of deaths, and devastated the social, financial and political entities around the world. Without an existing effective medical therapy, vaccines are urgently needed to avoid the spread of this disease. In this study, we propose an in silico deep learning approach for prediction and design of a multi-epitope vaccine (DeepVacPred). By combining the in silico immunoinformatics and...
9 CitationsSource
#1Chao Fang (AstraZeneca)H-Index: 2
#2Dong Xu (MU: University of Missouri)H-Index: 2
Last. Bolan Linghu (AstraZeneca)H-Index: 3
view all 5 authors...
Immuno-oncology (IO) therapies have transformed the therapeutic landscape of non-small cell lung cancer (NSCLC). However, patient responses to IO are variable and influenced by a heterogeneous combination of health, immune, and tumor factors. There is a pressing need to discover the distinct NSCLC subgroups that influence response. We have developed a deep patient graph convolutional network, we call "DeePaN", to discover NSCLC complexity across data modalities impacting IO benefit. DeePaN emplo...
1 CitationsSource
#1Debleena Paul (Government of India)H-Index: 2
#2Gaurav Sanap (Government of India)H-Index: 1
Last. Rakesh K. Tekade (Government of India)H-Index: 7
view all 6 authors...
Artificial Intelligence (AI) has recently started to gear-up its application in various sectors of the society with the pharmaceutical industry as a front-runner beneficiary. This review highlights the impactful use of AI in diverse areas of the pharmaceutical sectors viz., drug discovery and development, drug repurposing, improving pharmaceutical productivity, clinical trials, etc. to name a few, thus reducing the human workload as well as achieving targets in a short period. Crosstalk on the t...
4 CitationsSource
#1Emilio Dorigatti (LMU: Ludwig Maximilian University of Munich)H-Index: 2
#2Benjamin SchubertH-Index: 11
MOTIVATION Conceptually, epitope-based vaccine design poses two distinct problems: (i) selecting the best epitopes to elicit the strongest possible immune response and (ii) arranging and linking them through short spacer sequences to string-of-beads vaccines, so that their recovery likelihood during antigen processing is maximized. Current state-of-the-art approaches solve this design problem sequentially. Consequently, such approaches are unable to capture the inter-dependencies between the two...
2 CitationsSource
#1C Angwin (University of Exeter)
#1Catherine Angwin (University of Exeter)
Last. Andrew T. Hattersley (University of Exeter)H-Index: 157
view all 11 authors...
Introduction Pharmaceutical treatment options for patients with type 2 diabetes mellitus (T2DM) have increased to include multiple classes of oral glucose-lowering agents but without accompanying guidance on which of these may most benefit individual patients. Clinicians lack information for treatment intensification after first-line metformin therapy. Stratifying patients by simple clinical characteristics may improve care by targeting treatment options to those in whom they are most effective....
#1Wei Zhao (University of Texas MD Anderson Cancer Center)H-Index: 21
#2Jun Li (University of Texas MD Anderson Cancer Center)
Last. Han Liang (BCM: Baylor College of Medicine)H-Index: 65
view all 26 authors...
Summary Perturbation biology is a powerful approach to modeling quantitative cellular behaviors and understanding detailed disease mechanisms. However, large-scale protein response resources of cancer cell lines to perturbations are not available, resulting in a critical knowledge gap. Here we generated and compiled perturbed expression profiles of ∼210 clinically relevant proteins in >12,000 cancer cell line samples in response to ∼170 drug compounds using reverse-phase protein arrays. We show ...
4 CitationsSource
#1Brent M. Kuenzi (UCSD: University of California, San Diego)H-Index: 10
#2Jisoo Park (UCSD: University of California, San Diego)H-Index: 6
Last. Trey Ideker (UCSD: University of California, San Diego)H-Index: 98
view all 8 authors...
Summary Most drugs entering clinical trials fail, often related to an incomplete understanding of the mechanisms governing drug response. Machine learning techniques hold immense promise for better drug response predictions, but most have not reached clinical practice due to their lack of interpretability and their focus on monotherapies. We address these challenges by developing DrugCell, an interpretable deep learning model of human cancer cells trained on the responses of 1,235 tumor cell lin...
16 CitationsSource
#1Giovanna Morello (University of Catania)H-Index: 11
#2Salvatore Salomone (University of Catania)H-Index: 35
Last. Sebastiano Cavallaro (National Research Council)H-Index: 32
view all 5 authors...
Amyotrophic lateral sclerosis (ALS) is a devastating and fatal neurodegenerative disorder, caused by the degeneration of upper and lower motor neurons for which there is no truly effective cure. The lack of successful treatments can be well explained by the complex and heterogeneous nature of ALS, with patients displaying widely distinct clinical features and progression patterns, and distinct molecular mechanisms underlying the phenotypic heterogeneity. Thus, stratifying ALS patients into consi...
1 CitationsSource
Cited By0