1
|
Komissarov L, Manevski N, Groebke Zbinden K, Schindler T, Zitnik M, Sach-Peltason L. Actionable Predictions of Human Pharmacokinetics at the Drug Design Stage. Mol Pharm 2024. [PMID: 39132855 DOI: 10.1021/acs.molpharmaceut.4c00311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
We present a novel computational approach for predicting human pharmacokinetics (PK) that addresses the challenges of early stage drug design. Our study introduces and describes a large-scale data set of 11 clinical PK end points, encompassing over 2700 unique chemical structures to train machine learning models. To that end multiple advanced training strategies are compared, including the integration of in vitro data and a novel self-supervised pretraining task. In addition to the predictions, our final model provides meaningful epistemic uncertainties for every data point. This allows us to successfully identify regions of exceptional predictive performance, with an absolute average fold error (AAFE/geometric mean fold error) of less than 2.5 across multiple end points. Together, these advancements represent a significant leap toward actionable PK predictions, which can be utilized early on in the drug design process to expedite development and reduce reliance on nonclinical studies.
Collapse
Affiliation(s)
- Leonid Komissarov
- Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, Basel 4070, Switzerland
| | - Nenad Manevski
- Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, Basel 4070, Switzerland
| | - Katrin Groebke Zbinden
- Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, Basel 4070, Switzerland
| | - Torsten Schindler
- Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, Basel 4070, Switzerland
| | - Marinka Zitnik
- Harvard Medical School, Department of Biomedical Informatics, Boston, Massachusetts 02115, United States
| | - Lisa Sach-Peltason
- Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, Basel 4070, Switzerland
| |
Collapse
|
2
|
Walter M, Borghardt JM, Humbeck L, Skalic M. Multi-Task ADME/PK prediction at industrial scale: leveraging large and diverse experimentaldatasets. Mol Inform 2024:e202400079. [PMID: 38973777 DOI: 10.1002/minf.202400079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 04/10/2024] [Accepted: 05/04/2024] [Indexed: 07/09/2024]
Abstract
ADME (Absorption, Distribution, Metabolism, Excretion) properties are key parameters to judge whether a drug candidate exhibits a desired pharmacokinetic (PK) profile. In this study, we tested multi-task machine learning (ML) models to predict ADME and animal PK endpoints trained on in-house data generated at Boehringer Ingelheim. Models were evaluated both at the design stage of a compound (i. e., no experimental data of test compounds available) and at testing stage when a particular assay would be conducted (i. e., experimental data of earlier conducted assays may be available). Using realistic time-splits, we found a clear benefit in performance of multi-task graph-based neural network models over single-task model, which was even stronger when experimental data of earlier assays is available. In an attempt to explain the success of multi-task models, we found that especially endpoints with the largest numbers of data points (physicochemical endpoints, clearance in microsomes) are responsible for increased predictivity in more complex ADME and PK endpoints. In summary, our study provides insight into how data for multiple ADME/PK endpoints in a pharmaceutical company can be best leveraged to optimize predictivity of ML models.
Collapse
Affiliation(s)
- Moritz Walter
- Medicinal Chemistry Department, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Str. 65, 88397, Biberach an der Riss, Germany
| | - Jens M Borghardt
- Drug Discovery Sciences Department, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Str. 65, 88397, Biberach an der Riss, Germany
| | - Lina Humbeck
- Medicinal Chemistry Department, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Str. 65, 88397, Biberach an der Riss, Germany
| | - Miha Skalic
- Medicinal Chemistry Department, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Str. 65, 88397, Biberach an der Riss, Germany
| |
Collapse
|
3
|
Bassani D, Parrott NJ, Manevski N, Zhang JD. Another string to your bow: machine learning prediction of the pharmacokinetic properties of small molecules. Expert Opin Drug Discov 2024; 19:683-698. [PMID: 38727016 DOI: 10.1080/17460441.2024.2348157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 04/23/2024] [Indexed: 05/22/2024]
Abstract
INTRODUCTION Prediction of pharmacokinetic (PK) properties is crucial for drug discovery and development. Machine-learning (ML) models, which use statistical pattern recognition to learn correlations between input features (such as chemical structures) and target variables (such as PK parameters), are being increasingly used for this purpose. To embed ML models for PK prediction into workflows and to guide future development, a solid understanding of their applicability, advantages, limitations, and synergies with other approaches is necessary. AREAS COVERED This narrative review discusses the design and application of ML models to predict PK parameters of small molecules, especially in light of established approaches including in vitro-in vivo extrapolation (IVIVE) and physiologically based pharmacokinetic (PBPK) models. The authors illustrate scenarios in which the three approaches are used and emphasize how they enhance and complement each other. In particular, they highlight achievements, the state of the art and potentials of applying machine learning for PK prediction through a comphrehensive literature review. EXPERT OPINION ML models, when carefully crafted, regularly updated, and appropriately used, empower users to prioritize molecules with favorable PK properties. Informed practitioners can leverage these models to improve the efficiency of drug discovery and development process.
Collapse
Affiliation(s)
- Davide Bassani
- Pharmaceutical Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Neil John Parrott
- Pharmaceutical Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Nenad Manevski
- Pharmaceutical Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Jitao David Zhang
- Pharmaceutical Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| |
Collapse
|
4
|
Guo W, Dong Y, Hao GF. Transfer learning empowers accurate pharmacokinetics prediction of small samples. Drug Discov Today 2024; 29:103946. [PMID: 38460571 DOI: 10.1016/j.drudis.2024.103946] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 02/22/2024] [Accepted: 03/05/2024] [Indexed: 03/11/2024]
Abstract
Accurate assessment of pharmacokinetic (PK) properties is crucial for selecting optimal candidates and avoiding downstream failures. Transfer learning is an innovative machine learning approach enabling high-throughput prediction with limited data. Recently, transfer learning methods showed promise in predicting ADME/PK parameters. Given the prolific growth of research on transfer learning for PK prediction, a comprehensive review of its advantages and challenges is imperative. This study explores the fundamentals, classifications, toolkits and applications of various transfer learning techniques for PK prediction, demonstrating their utility through three practical case studies. This work will serve as a reference for drug design researchers.
Collapse
Affiliation(s)
- Wenbo Guo
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang 550025, China
| | - Yawen Dong
- School of Pharmaceutical Sciences, Guizhou University, Guiyang 550025, China.
| | - Ge-Fei Hao
- National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang 550025, China.
| |
Collapse
|
5
|
Lombardo F, Bentzien J, Berellini G, Muegge I. Prediction of Human Clearance Using In Silico Models with Reduced Bias. Mol Pharm 2024; 21:1192-1203. [PMID: 38285644 DOI: 10.1021/acs.molpharmaceut.3c00812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2024]
Abstract
Predicting human clearance with high accuracy from in silico-derived parameters alone is highly desirable, as it is fast, saves in vitro resources, and is animal-sparing. We derived random forest (RF) models from 1340 compounds with human intravenous pharmacokinetic (PK) data, the largest data set publicly available today. To assess the general applicability of the RF models, we systematically removed structural-therapeutic class analogues and other compounds with structural similarity from the training sets. For a quasi-prospective test set of 343 compounds, we show that RF models devoid of structurally similar compounds in the training set predict human clearance with a geometric mean fold error (GMFE) of 3.3. While the observed GMFE illustrates how difficult it is to generate a useful model that is broadly applicable, we posit that our RF models yield a more realistic assessment of how well human clearance can be predicted prospectively. We deployed the conformal prediction formalism to assess the model applicability and to determine the prediction confidence intervals for each prediction. We observed that clearance can be predicted better for renally cleared compounds than for other clearance mechanisms. We show that applying a classification model for predicting renal clearance identifies a subset of compounds for which clearance can be predicted with higher accuracy, yielding a GMFE of 2.3. In addition, our in silico RF human clearance models compared well to models derived from scaling human hepatocytes or preclinical in vivo data.
Collapse
Affiliation(s)
- Franco Lombardo
- CmaxDMPK, LLC, Framingham , Massachusetts 01701, United States
| | - Jörg Bentzien
- Alkermes Inc., 852 Winter Street, Waltham, Massachusetts 02451, United States
| | - Giuliano Berellini
- Alkermes Inc., 852 Winter Street, Waltham, Massachusetts 02451, United States
| | - Ingo Muegge
- Alkermes Inc., 852 Winter Street, Waltham, Massachusetts 02451, United States
| |
Collapse
|
6
|
Shen T, Guo J, Han Z, Zhang G, Liu Q, Si X, Wang D, Wu S, Xia J. AutoMolDesigner for Antibiotic Discovery: An AI-Based Open-Source Software for Automated Design of Small-Molecule Antibiotics. J Chem Inf Model 2024; 64:575-583. [PMID: 38265916 DOI: 10.1021/acs.jcim.3c01562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2024]
Abstract
Discovery of small-molecule antibiotics with novel chemotypes serves as one of the essential strategies to address antibiotic resistance. Although a considerable number of computational tools committed to molecular design have been reported, there is a deficit in holistic and efficient tools specifically developed for small-molecule antibiotic discovery. To address this issue, we report AutoMolDesigner, a computational modeling software dedicated to small-molecule antibiotic design. It is a generalized framework comprising two functional modules, i.e., generative-deep-learning-enabled molecular generation and automated machine-learning-based antibacterial activity/property prediction, wherein individually trained models and curated datasets are out-of-the-box for whole-cell-based antibiotic screening and design. It is open-source, thus allowing for the incorporation of new features for flexible use. Unlike most software programs based on Linux and command lines, this application equipped with a Qt-based graphical user interface can be run on personal computers with multiple operating systems, making it much easier to use for experimental scientists. The software and related materials are freely available at GitHub (https://github.com/taoshen99/AutoMolDesigner) and Zenodo (https://zenodo.org/record/10097899).
Collapse
Affiliation(s)
- Tao Shen
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Jiale Guo
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Zunsheng Han
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Gao Zhang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Qingxin Liu
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
- School of Pharmacy, Jiangsu Ocean University, Lianyungang, Jiangsu 222005, China
| | - Xinxin Si
- School of Pharmacy, Jiangsu Ocean University, Lianyungang, Jiangsu 222005, China
| | - Dongmei Wang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Song Wu
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| | - Jie Xia
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China
| |
Collapse
|
7
|
Baygi SF, Barupal DK. IDSL_MINT: a deep learning framework to predict molecular fingerprints from mass spectra. J Cheminform 2024; 16:8. [PMID: 38238779 PMCID: PMC10797927 DOI: 10.1186/s13321-024-00804-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 01/14/2024] [Indexed: 01/22/2024] Open
Abstract
The majority of tandem mass spectrometry (MS/MS) spectra in untargeted metabolomics and exposomics studies lack any annotation. Our deep learning framework, Integrated Data Science Laboratory for Metabolomics and Exposomics-Mass INTerpreter (IDSL_MINT) can translate MS/MS spectra into molecular fingerprint descriptors. IDSL_MINT allows users to leverage the power of the transformer model for mass spectrometry data, similar to the large language models. Models are trained on user-provided reference MS/MS libraries via any customizable molecular fingerprint descriptors. IDSL_MINT was benchmarked using the LipidMaps database and improved the annotation rate of a test study for MS/MS spectra that were not originally annotated using existing mass spectral libraries. IDSL_MINT may improve the overall annotation rates in untargeted metabolomics and exposomics studies. The IDSL_MINT framework and tutorials are available in the GitHub repository at https://github.com/idslme/IDSL_MINT .Scientific contribution statement.Structural annotation of MS/MS spectra from untargeted metabolomics and exposomics datasets is a major bottleneck in gaining new biological insights. Machine learning models to convert spectra into molecular fingerprints can help in the annotation process. Here, we present IDSL_MINT, a new, easy-to-use and customizable deep-learning framework to train and utilize new models to predict molecular fingerprints from spectra for the compound annotation workflows.
Collapse
Affiliation(s)
- Sadjad Fakouri Baygi
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, CAM Building, 3rd Floor, 17 E 102 St, New York, NY, 10029, USA
| | - Dinesh Kumar Barupal
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, CAM Building, 3rd Floor, 17 E 102 St, New York, NY, 10029, USA.
| |
Collapse
|
8
|
Tautermann CS, Borghardt JM, Pfau R, Zentgraf M, Weskamp N, Sauer A. Towards holistic Compound Quality Scores: Extending ligand efficiency indices with compound pharmacokinetic characteristics. Drug Discov Today 2023; 28:103758. [PMID: 37660984 DOI: 10.1016/j.drudis.2023.103758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 08/17/2023] [Accepted: 08/28/2023] [Indexed: 09/05/2023]
Abstract
The suitability of small molecules as oral drugs is often assessed by simple physicochemical rules, the application of ligand efficiency scores or by composite scores based on physicochemical compound properties. These rules and scores are empirical and typically lack mechanistic background, such as information on pharmacokinetics (PK). We introduce new types of Compound Quality Scores (CQS, specifically called dose scores and cmax scores), which explicitly include predicted or, when available, experimental PK parameters and combine these with on-target potency. These CQS scores are surrogates for an estimated dose and corresponding cmax and allow prioritizing of compounds within test cascades as well as before synthesis. We demonstrate the complementarity and, in most cases, superior performance relative to existing efficiency metrics by project examples.
Collapse
Affiliation(s)
- Christofer S Tautermann
- Boehringer Ingelheim Pharma GmbH & Co. KG, Medicinal Chemistry, Birkendorfer Strasse 65, Biberach 88397, Germany; Department of General, Inorganic and Theoretical Chemistry, University of Innsbruck, Innsbruck 6020, Austria.
| | - Jens M Borghardt
- Boehringer Ingelheim Pharma GmbH & Co. KG, Drug Discovery Sciences, Birkendorfer Strasse 65, Biberach 88397, Germany.
| | - Roland Pfau
- Boehringer Ingelheim Pharma GmbH & Co. KG, Medicinal Chemistry, Birkendorfer Strasse 65, Biberach 88397, Germany; Boehringer Ingelheim Pharma GmbH & Co. KG, CNS Research, Birkendorfer Strasse 65, Biberach 88397, Germany.
| | - Matthias Zentgraf
- Boehringer Ingelheim Pharma GmbH & Co. KG, Discovery Research Coordination Germany, Birkendorfer Strasse 65, Biberach 88397, Germany.
| | - Nils Weskamp
- Boehringer Ingelheim Pharma GmbH & Co. KG, Medicinal Chemistry, Birkendorfer Strasse 65, Biberach 88397, Germany.
| | - Achim Sauer
- Boehringer Ingelheim Pharma GmbH & Co. KG, Drug Discovery Sciences, Birkendorfer Strasse 65, Biberach 88397, Germany.
| |
Collapse
|
9
|
Andrews-Morger A, Reutlinger M, Parrott N, Olivares-Morales A. A Machine Learning Framework to Improve Rat Clearance Predictions and Inform Physiologically Based Pharmacokinetic Modeling. Mol Pharm 2023; 20:5052-5065. [PMID: 37713584 DOI: 10.1021/acs.molpharmaceut.3c00374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/17/2023]
Abstract
During drug discovery and development, achieving appropriate pharmacokinetics is key to establishment of the efficacy and safety of new drugs. Physiologically based pharmacokinetic (PBPK) models integrating in vitro-to-in vivo extrapolation have become an essential in silico tool to achieve this goal. In this context, the most important and probably most challenging pharmacokinetic parameter to estimate is the clearance. Recent work on high-throughput PBPK modeling during drug discovery has shown that a good estimate of the unbound intrinsic clearance (CLint,u,) is the key factor for useful PBPK application. In this work, three different machine learning-based strategies were explored to predict the rat CLint,u as the input into PBPK. Therefore, in vivo and in vitro data was collected for a total of 2639 proprietary compounds. The strategies were compared to the standard in vitro bottom-up approach. Using the well-stirred liver model to back-calculate in vivo CLint,u from in vivo rat clearance and then training a machine learning model on this CLint,u led to more accurate clearance predictions (absolute average fold error (AAFE) 3.1 in temporal cross-validation) than the bottom-up approach (AAFE 3.6-16, depending on the scaling method) and has the advantage that no experimental in vitro data is needed. However, building a machine learning model on the bias between the back-calculated in vivo CLint,u and the bottom-up scaled in vitro CLint,u also performed well. For example, using unbound hepatocyte scaling, adding the bias prediction improved the AAFE in the temporal cross-validation from 16 for bottom-up to 2.9 together with the bias prediction. Similarly, the log Pearson r2 improved from 0.1 to 0.29. Although it would still require in vitro measurement of CLint,u., using unbound scaling for the bottom-up approach, the need for correction of the fu,inc by fu,p data is circumvented. While the above-described ML models were built on all data points available per approach, it is discussed that evaluation comparison across all approaches could only be performed on a subset because ca. 75% of the molecules had missing or unquantifiable measurements of the fraction unbound in plasma or in vitro unbound intrinsic clearance, or they dropped out due to the blood-flow limitation assumed by the well-stirred model. Advantageously, by predicting CLint,u as the input into PBPK, existing workflows can be reused and the prediction of the in vivo clearance and other PK parameters can be improved.
Collapse
Affiliation(s)
- Andrea Andrews-Morger
- Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070 Basel, Switzerland
| | - Michael Reutlinger
- Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070 Basel, Switzerland
| | - Neil Parrott
- Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070 Basel, Switzerland
| | - Andrés Olivares-Morales
- Roche Pharmaceutical Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, 4070 Basel, Switzerland
| |
Collapse
|
10
|
Ekins S, Lane TR, Urbina F, Puhl AC. In silico ADME/tox comes of age: twenty years later. Xenobiotica 2023:1-7. [PMID: 37539466 PMCID: PMC10850432 DOI: 10.1080/00498254.2023.2245049] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 08/01/2023] [Accepted: 08/02/2023] [Indexed: 08/05/2023]
Abstract
In the early 2000s pharmaceutical drug discovery was beginning to use computational approaches for absorption, distribution, metabolism, excretion and toxicity (ADME/Tox, also known as ADMET) prediction. This emphasis on prediction was an effort to reduce the risk of later stage failures from ADME/Tox.Much has been written in the intervening twenty plus years and significant expenditure has occurred in companies developing these in silico capabilities which can be gleaned from publications. It is therefore an appropriate time to briefly reflect on what was proposed then and what the reality is today.20 years ago, we tended to optimise bioactivity and perhaps one ADME/Tox property at a time. Previously pharmaceutical companies needed a whole infrastructure for models - in silico and in vitro experts, IT, champions on a project team, educators and management support. Now we are in the age of generative de novo design where bioactivity and many ADME/Tox properties can be optimised and large language model technologies are available.There are also some challenges such as the focus on very large molecules which may be outside of current ADME/Tox models.We provide an opportunity to look forward with the increasing public data for ADME/Tox as well as expanded types of algorithms available.
Collapse
Affiliation(s)
- Sean Ekins
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC 27606, USA
| | - Thomas R. Lane
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC 27606, USA
| | - Fabio Urbina
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC 27606, USA
| | - Ana C. Puhl
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC 27606, USA
| |
Collapse
|