1
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Sela M, Church JR, Schapiro I, Schneidman-Duhovny D. RhoMax: Computational Prediction of Rhodopsin Absorption Maxima Using Geometric Deep Learning. J Chem Inf Model 2024; 64:4630-4639. [PMID: 38829021 PMCID: PMC11200256 DOI: 10.1021/acs.jcim.4c00467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 05/15/2024] [Accepted: 05/17/2024] [Indexed: 06/05/2024]
Abstract
Microbial rhodopsins (MRs) are a diverse and abundant family of photoactive membrane proteins that serve as model systems for biophysical techniques. Optogenetics utilizes genetic engineering to insert specialized proteins into specific neurons or brain regions, allowing for manipulation of their activity through light and enabling the mapping and control of specific brain areas in living organisms. The obstacle of optogenetics lies in the fact that light has a limited ability to penetrate biological tissues, particularly blue light in the visible spectrum. Despite this challenge, most optogenetic systems rely on blue light due to the scarcity of red-shifted opsins. Finding additional red-shifted rhodopsins would represent a major breakthrough in overcoming the challenge of limited light penetration in optogenetics. However, determining the wavelength absorption maxima for rhodopsins based on their protein sequence is a significant hurdle. Current experimental methods are time-consuming, while computational methods lack accuracy. The paper introduces a new computational approach called RhoMax that utilizes structure-based geometric deep learning to predict the absorption wavelength of rhodopsins solely based on their sequences. The method takes advantage of AlphaFold2 for accurate modeling of rhodopsin structures. Once trained on a balanced train set, RhoMax rapidly and precisely predicted the maximum absorption wavelength of more than half of the sequences in our test set with an accuracy of 0.03 eV. By leveraging computational methods for absorption maxima determination, we can drastically reduce the time needed for designing new red-shifted microbial rhodopsins, thereby facilitating advances in the field of optogenetics.
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Affiliation(s)
- Meitar Sela
- The
Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Jonathan R. Church
- Fritz
Haber Center for Molecular Dynamics Research, Institute of Chemistry, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Igor Schapiro
- Fritz
Haber Center for Molecular Dynamics Research, Institute of Chemistry, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
| | - Dina Schneidman-Duhovny
- The
Rachel and Selim Benin School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
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2
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Tang X, Zepeda-Nuñez L, Yang S, Zhao Z, Solís-Lemus C. Novel symmetry-preserving neural network model for phylogenetic inference. BIOINFORMATICS ADVANCES 2024; 4:vbae022. [PMID: 38638281 PMCID: PMC11026143 DOI: 10.1093/bioadv/vbae022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 01/29/2024] [Accepted: 02/17/2024] [Indexed: 04/20/2024]
Abstract
Motivation Scientists world-wide are putting together massive efforts to understand how the biodiversity that we see on Earth evolved from single-cell organisms at the origin of life and this diversification process is represented through the Tree of Life. Low sampling rates and high heterogeneity in the rate of evolution across sites and lineages produce a phenomenon denoted "long branch attraction" (LBA) in which long nonsister lineages are estimated to be sisters regardless of their true evolutionary relationship. LBA has been a pervasive problem in phylogenetic inference affecting different types of methodologies from distance-based to likelihood-based. Results Here, we present a novel neural network model that outperforms standard phylogenetic methods and other neural network implementations under LBA settings. Furthermore, unlike existing neural network models in phylogenetics, our model naturally accounts for the tree isomorphisms via permutation invariant functions which ultimately result in lower memory and allows the seamless extension to larger trees. Availability and implementation We implement our novel theory on an open-source publicly available GitHub repository: https://github.com/crsl4/nn-phylogenetics.
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Affiliation(s)
- Xudong Tang
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI 53706, United States
- Department of Statistics, University of Wisconsin-Madison, Madison, WI 53706, United States
| | - Leonardo Zepeda-Nuñez
- Department of Mathematics, University of Wisconsin-Madison, Madison, WI 53706, United States
| | - Shengwen Yang
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI 53706, United States
- Department of Statistics, University of Wisconsin-Madison, Madison, WI 53706, United States
| | - Zelin Zhao
- Department of Mathematics, University of Wisconsin-Madison, Madison, WI 53706, United States
| | - Claudia Solís-Lemus
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI 53706, United States
- Department of Plant Pathology, University of Wisconsin-Madison, Madison, WI 53706, United States
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3
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Gangwal A, Ansari A, Ahmad I, Azad AK, Kumarasamy V, Subramaniyan V, Wong LS. Generative artificial intelligence in drug discovery: basic framework, recent advances, challenges, and opportunities. Front Pharmacol 2024; 15:1331062. [PMID: 38384298 PMCID: PMC10879372 DOI: 10.3389/fphar.2024.1331062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 01/17/2024] [Indexed: 02/23/2024] Open
Abstract
There are two main ways to discover or design small drug molecules. The first involves fine-tuning existing molecules or commercially successful drugs through quantitative structure-activity relationships and virtual screening. The second approach involves generating new molecules through de novo drug design or inverse quantitative structure-activity relationship. Both methods aim to get a drug molecule with the best pharmacokinetic and pharmacodynamic profiles. However, bringing a new drug to market is an expensive and time-consuming endeavor, with the average cost being estimated at around $2.5 billion. One of the biggest challenges is screening the vast number of potential drug candidates to find one that is both safe and effective. The development of artificial intelligence in recent years has been phenomenal, ushering in a revolution in many fields. The field of pharmaceutical sciences has also significantly benefited from multiple applications of artificial intelligence, especially drug discovery projects. Artificial intelligence models are finding use in molecular property prediction, molecule generation, virtual screening, synthesis planning, repurposing, among others. Lately, generative artificial intelligence has gained popularity across domains for its ability to generate entirely new data, such as images, sentences, audios, videos, novel chemical molecules, etc. Generative artificial intelligence has also delivered promising results in drug discovery and development. This review article delves into the fundamentals and framework of various generative artificial intelligence models in the context of drug discovery via de novo drug design approach. Various basic and advanced models have been discussed, along with their recent applications. The review also explores recent examples and advances in the generative artificial intelligence approach, as well as the challenges and ongoing efforts to fully harness the potential of generative artificial intelligence in generating novel drug molecules in a faster and more affordable manner. Some clinical-level assets generated form generative artificial intelligence have also been discussed in this review to show the ever-increasing application of artificial intelligence in drug discovery through commercial partnerships.
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Affiliation(s)
- Amit Gangwal
- Department of Natural Product Chemistry, Shri Vile Parle Kelavani Mandal’s Institute of Pharmacy, Dhule, Maharashtra, India
| | - Azim Ansari
- Computer Aided Drug Design Center Shri Vile Parle Kelavani Mandal’s Institute of Pharmacy, Dhule, Maharashtra, India
| | - Iqrar Ahmad
- Department of Pharmaceutical Chemistry, Prof. Ravindra Nikam College of Pharmacy, Dhule, India
| | - Abul Kalam Azad
- Faculty of Pharmacy, University College of MAIWP International, Batu Caves, Malaysia
| | - Vinoth Kumarasamy
- Department of Parasitology and Medical Entomology, Faculty of Medicine, Universiti Kebangsaan Malaysia, Cheras, Malaysia
| | - Vetriselvan Subramaniyan
- Pharmacology Unit, Jeffrey Cheah School of Medicine and Health Sciences, Monash University Malaysia, Selangor, Malaysia
- School of Bioengineering and Biosciences, Lovely Professional University, Phagwara, Punjab, India
| | - Ling Shing Wong
- Faculty of Health and Life Sciences, INTI International University, Nilai, Malaysia
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4
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Iwata H, Nakai T, Koyama T, Matsumoto S, Kojima R, Okuno Y. VGAE-MCTS: A New Molecular Generative Model Combining the Variational Graph Auto-Encoder and Monte Carlo Tree Search. J Chem Inf Model 2023; 63:7392-7400. [PMID: 37993764 PMCID: PMC10716893 DOI: 10.1021/acs.jcim.3c01220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 11/03/2023] [Accepted: 11/03/2023] [Indexed: 11/24/2023]
Abstract
Molecular generation is crucial for advancing drug discovery, materials science, and chemical exploration. It expedites the search for new drug candidates, facilitates tailored material creation, and enhances our understanding of molecular diversity. By employing artificial intelligence techniques such as molecular generative models based on molecular graphs, researchers have tackled the challenge of identifying efficient molecules with desired properties. Here, we propose a new molecular generative model combining a graph-based deep neural network and a reinforcement learning technique. We evaluated the validity, novelty, and optimized physicochemical properties of the generated molecules. Importantly, the model explored uncharted regions of chemical space, allowing for the efficient discovery and design of new molecules. This innovative approach has considerable potential to revolutionize drug discovery, materials science, and chemical research for accelerating scientific innovation. By leveraging advanced techniques and exploring previously unexplored chemical spaces, this study offers promising prospects for the efficient discovery and design of new molecules in the field of drug development.
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Affiliation(s)
- Hiroaki Iwata
- Graduate
School of Medicine, Kyoto University, 53 Shogoin-kawaharacho, Sakyo-ku, Kyoto-shi, Kyoto 606-8507, Japan
| | - Taichi Nakai
- Graduate
School of Medicine, Kyoto University, 53 Shogoin-kawaharacho, Sakyo-ku, Kyoto-shi, Kyoto 606-8507, Japan
| | - Takuto Koyama
- Graduate
School of Medicine, Kyoto University, 53 Shogoin-kawaharacho, Sakyo-ku, Kyoto-shi, Kyoto 606-8507, Japan
| | - Shigeyuki Matsumoto
- Graduate
School of Medicine, Kyoto University, 53 Shogoin-kawaharacho, Sakyo-ku, Kyoto-shi, Kyoto 606-8507, Japan
| | - Ryosuke Kojima
- Graduate
School of Medicine, Kyoto University, 53 Shogoin-kawaharacho, Sakyo-ku, Kyoto-shi, Kyoto 606-8507, Japan
| | - Yasushi Okuno
- Graduate
School of Medicine, Kyoto University, 53 Shogoin-kawaharacho, Sakyo-ku, Kyoto-shi, Kyoto 606-8507, Japan
- HPC-
and AI-driven Drug Development Platform Division, RIKEN Center for Computational Science, Kobe-shi, Hyogo 650-0047, Japan
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5
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Ilnicka A, Schneider G. Designing molecules with autoencoder networks. NATURE COMPUTATIONAL SCIENCE 2023; 3:922-933. [PMID: 38177601 DOI: 10.1038/s43588-023-00548-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 10/03/2023] [Indexed: 01/06/2024]
Abstract
Autoencoders are versatile tools in molecular informatics. These unsupervised neural networks serve diverse tasks such as data-driven molecular representation and constructive molecular design. This Review explores their algorithmic foundations and applications in drug discovery, highlighting the most active areas of development and the contributions autoencoder networks have made in advancing this field. We also explore the challenges and prospects concerning the utilization of autoencoders and the various adaptations of this neural network architecture in molecular design.
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Affiliation(s)
- Agnieszka Ilnicka
- Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich, Switzerland
| | - Gisbert Schneider
- Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich, Switzerland.
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6
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Chen L, Yu L, Gao L. Potent antibiotic design via guided search from antibacterial activity evaluations. Bioinformatics 2023; 39:7008322. [PMID: 36707990 PMCID: PMC9897189 DOI: 10.1093/bioinformatics/btad059] [Citation(s) in RCA: 39] [Impact Index Per Article: 39.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 01/14/2023] [Accepted: 01/25/2023] [Indexed: 01/29/2023] Open
Abstract
MOTIVATION The emergence of drug-resistant bacteria makes the discovery of new antibiotics an urgent issue, but finding new molecules with the desired antibacterial activity is an extremely difficult task. To address this challenge, we established a framework, MDAGS (Molecular Design via Attribute-Guided Search), to optimize and generate potent antibiotic molecules. RESULTS By designing the antibacterial activity latent space and guiding the optimization of functional compounds based on this space, the model MDAGS can generate novel compounds with desirable antibacterial activity without the need for extensive expensive and time-consuming evaluations. Compared with existing antibiotics, candidate antibacterial compounds generated by MDAGS always possessed significantly better antibacterial activity and ensured high similarity. Furthermore, although without explicit constraints on similarity to known antibiotics, these candidate antibacterial compounds all exhibited the highest structural similarity to antibiotics of expected function in the DrugBank database query. Overall, our approach provides a viable solution to the problem of bacterial drug resistance. AVAILABILITY AND IMPLEMENTATION Code of the model and datasets can be downloaded from GitHub (https://github.com/LiangYu-Xidian/MDAGS). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Lu Chen
- School of Computer Science and Technology, Xidian University, Xi'an 710071, Shaanxi, China
| | - Liang Yu
- School of Computer Science and Technology, Xidian University, Xi'an 710071, Shaanxi, China
| | - Lin Gao
- School of Computer Science and Technology, Xidian University, Xi'an 710071, Shaanxi, China
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7
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Abate C, Decherchi S, Cavalli A. Graph neural networks for conditional de novo drug design. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2023. [DOI: 10.1002/wcms.1651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Affiliation(s)
- Carlo Abate
- Fondazione Istituto Italiano di Tecnologia Genoa Italy
- Università degli Studi di Bologna Bologna Italy
| | | | - Andrea Cavalli
- Fondazione Istituto Italiano di Tecnologia Genoa Italy
- Università degli Studi di Bologna Bologna Italy
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8
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Li C, Wang C, Sun M, Zeng Y, Yuan Y, Gou Q, Wang G, Guo Y, Pu X. Correlated RNN Framework to Quickly Generate Molecules with Desired Properties for Energetic Materials in the Low Data Regime. J Chem Inf Model 2022; 62:4873-4887. [PMID: 35998331 DOI: 10.1021/acs.jcim.2c00997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Motivated by the challenging of deep learning on the low data regime and the urgent demand for intelligent design on highly energetic materials, we explore a correlated deep learning framework, which consists of three recurrent neural networks (RNNs) correlated by the transfer learning strategy, to efficiently generate new energetic molecules with a high detonation velocity in the case of very limited data available. To avoid the dependence on the external big data set, data augmentation by fragment shuffling of 303 energetic compounds is utilized to produce 500,000 molecules to pretrain RNN, through which the model can learn sufficient structure knowledge. Then the pretrained RNN is fine-tuned by focusing on the 303 energetic compounds to generate 7153 molecules similar to the energetic compounds. In order to more reliably screen the molecules with a high detonation velocity, the SMILE enumeration augmentation coupled with the pretrained knowledge is utilized to build an RNN-based prediction model, through which R2 is boosted from 0.4446 to 0.9572. The comparable performance with the transfer learning strategy based on an existing big database (ChEMBL) to produce the energetic molecules and drug-like ones further supports the effectiveness and generality of our strategy in the low data regime. High-precision quantum mechanics calculations further confirm that 35 new molecules present a higher detonation velocity and lower synthetic accessibility than the classic explosive RDX, along with good thermal stability. In particular, three new molecules are comparable to caged CL-20 in the detonation velocity. All the source codes and the data set are freely available at https://github.com/wangchenghuidream/RNNMGM.
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Affiliation(s)
- Chuan Li
- College of Computer Science, Sichuan University, Chengdu 610064, China
| | - Chenghui Wang
- College of Computer Science, Sichuan University, Chengdu 610064, China
| | - Ming Sun
- College of Chemistry, Sichuan University, Chengdu 610064, China
| | - Yan Zeng
- College of Computer Science, Sichuan University, Chengdu 610064, China
| | - Yuan Yuan
- College of Management, Southwest University for Nationalities, Chengdu 610041, China
| | - Qiaolin Gou
- College of Chemistry, Sichuan University, Chengdu 610064, China
| | - Guangchuan Wang
- College of Computer Science, Sichuan University, Chengdu 610064, China
| | - Yanzhi Guo
- College of Chemistry, Sichuan University, Chengdu 610064, China
| | - Xuemei Pu
- College of Chemistry, Sichuan University, Chengdu 610064, China
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9
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Alharbi WS, Rashid M. A review of deep learning applications in human genomics using next-generation sequencing data. Hum Genomics 2022; 16:26. [PMID: 35879805 PMCID: PMC9317091 DOI: 10.1186/s40246-022-00396-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 07/12/2022] [Indexed: 12/02/2022] Open
Abstract
Genomics is advancing towards data-driven science. Through the advent of high-throughput data generating technologies in human genomics, we are overwhelmed with the heap of genomic data. To extract knowledge and pattern out of this genomic data, artificial intelligence especially deep learning methods has been instrumental. In the current review, we address development and application of deep learning methods/models in different subarea of human genomics. We assessed over- and under-charted area of genomics by deep learning techniques. Deep learning algorithms underlying the genomic tools have been discussed briefly in later part of this review. Finally, we discussed briefly about the late application of deep learning tools in genomic. Conclusively, this review is timely for biotechnology or genomic scientists in order to guide them why, when and how to use deep learning methods to analyse human genomic data.
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Affiliation(s)
- Wardah S Alharbi
- Department of AI and Bioinformatics, King Abdullah International Medical Research Center (KAIMRC), King Saud Bin Abdulaziz University for Health Sciences (KSAU-HS), King Abdulaziz Medical City, Ministry of National Guard Health Affairs, P.O. Box 22490, Riyadh, 11426, Saudi Arabia
| | - Mamoon Rashid
- Department of AI and Bioinformatics, King Abdullah International Medical Research Center (KAIMRC), King Saud Bin Abdulaziz University for Health Sciences (KSAU-HS), King Abdulaziz Medical City, Ministry of National Guard Health Affairs, P.O. Box 22490, Riyadh, 11426, Saudi Arabia.
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10
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MSNovelist: de novo structure generation from mass spectra. Nat Methods 2022; 19:865-870. [PMID: 35637304 PMCID: PMC9262714 DOI: 10.1038/s41592-022-01486-3] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 04/07/2022] [Indexed: 12/29/2022]
Abstract
Current methods for structure elucidation of small molecules rely on finding similarity with spectra of known compounds, but do not predict structures de novo for unknown compound classes. We present MSNovelist, which combines fingerprint prediction with an encoder–decoder neural network to generate structures de novo solely from tandem mass spectrometry (MS2) spectra. In an evaluation with 3,863 MS2 spectra from the Global Natural Product Social Molecular Networking site, MSNovelist predicted 25% of structures correctly on first rank, retrieved 45% of structures overall and reproduced 61% of correct database annotations, without having ever seen the structure in the training phase. Similarly, for the CASMI 2016 challenge, MSNovelist correctly predicted 26% and retrieved 57% of structures, recovering 64% of correct database annotations. Finally, we illustrate the application of MSNovelist in a bryophyte MS2 dataset, in which de novo structure prediction substantially outscored the best database candidate for seven spectra. MSNovelist is ideally suited to complement library-based annotation in the case of poorly represented analyte classes and novel compounds. MSNovelist combines fingerprint prediction with an encoder–decoder neural network for de novo structure generation of small molecules from mass spectra.
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11
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Yu HK, Yu HC. Powerful molecule generation with simple ConvNet. Bioinformatics 2022; 38:3438-3443. [DOI: 10.1093/bioinformatics/btac332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 04/21/2022] [Accepted: 05/15/2022] [Indexed: 11/12/2022] Open
Abstract
Abstract
Motivation
Automated molecule generation is a crucial step in in-silico drug discovery. Graph-based generation algorithms have seen significant progress over recent years. However, they are often complex to implement, hard to train and can under-perform when generating long-sequence molecules. The development of a simple and powerful alternative can help improve practicality of automated drug discovery method.
Results
We proposed a ConvNet-based sequential graph generation algorithm. The molecular graph generation problem is reformulated as a sequence of simple classification tasks. At each step, a convolutional neural network operates on a sub-graph that is generated at previous step, and predicts/classifies an atom/bond adding action to populate the input sub-graph. The proposed model is pretrained by learning to sequentially reconstruct existing molecules. The pretrained model is abbreviated as SEEM (structural encoder for engineering molecules). It is then fine-tuned with reinforcement learning to generate molecules with improved properties. The fine-tuned model is named SEED (structural encoder for engineering drug-like-molecules). The proposed models have demonstrated competitive performance comparing to 16 state-of-the-art baselines on three benchmark datasets.
Availability and implementation
Code is available at https://github.com/yuh8/SEEM and https://github.com/yuh8/SEED. QM9 dataset is availble at http://quantum-machine.org/datasets/, ZINC250k dataset is availble at https://raw.githubusercontent.com/aspuru-guzik-group/chemical_vae/master/models/zinc_properties/250k_rndm_zinc_drugs_clean_3.csv, and ChEMBL dataset is availble at https://www.ebi.ac.uk/chembl/.
Supplementary information
Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Hongyang K Yu
- AI Drug Discovery, Anticancer Bioscience Ltd , Chengdu, China
| | - Hongjiang C Yu
- AI Drug Discovery, Anticancer Bioscience Ltd , Chengdu, China
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12
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Bilodeau C, Jin W, Jaakkola T, Barzilay R, Jensen KF. Generative models for molecular discovery: Recent advances and challenges. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2022. [DOI: 10.1002/wcms.1608] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Camille Bilodeau
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Wengong Jin
- Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Tommi Jaakkola
- Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Regina Barzilay
- Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Klavs F. Jensen
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
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13
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Polanski J. Unsupervised Learning in Drug Design from Self-Organization to Deep Chemistry. Int J Mol Sci 2022; 23:2797. [PMID: 35269939 PMCID: PMC8910896 DOI: 10.3390/ijms23052797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 02/27/2022] [Accepted: 02/27/2022] [Indexed: 12/10/2022] Open
Abstract
The availability of computers has brought novel prospects in drug design. Neural networks (NN) were an early tool that cheminformatics tested for converting data into drugs. However, the initial interest faded for almost two decades. The recent success of Deep Learning (DL) has inspired a renaissance of neural networks for their potential application in deep chemistry. DL targets direct data analysis without any human intervention. Although back-propagation NN is the main algorithm in the DL that is currently being used, unsupervised learning can be even more efficient. We review self-organizing maps (SOM) in mapping molecular representations from the 1990s to the current deep chemistry. We discovered the enormous efficiency of SOM not only for features that could be expected by humans, but also for those that are not trivial to human chemists. We reviewed the DL projects in the current literature, especially unsupervised architectures. DL appears to be efficient in pattern recognition (Deep Face) or chess (Deep Blue). However, an efficient deep chemistry is still a matter for the future. This is because the availability of measured property data in chemistry is still limited.
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Affiliation(s)
- Jaroslaw Polanski
- Institute of Chemistry, Faculty of Science and Technology, University of Silesia, Szkolna 9, 40-006 Katowice, Poland
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14
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Ragoza M, Masuda T, Koes DR. Generating 3D molecules conditional on receptor binding sites with deep generative models. Chem Sci 2022; 13:2701-2713. [PMID: 35356675 PMCID: PMC8890264 DOI: 10.1039/d1sc05976a] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 02/06/2022] [Indexed: 11/22/2022] Open
Abstract
The goal of structure-based drug discovery is to find small molecules that bind to a given target protein. Deep learning has been used to generate drug-like molecules with certain cheminformatic properties, but has not yet been applied to generating 3D molecules predicted to bind to proteins by sampling the conditional distribution of protein-ligand binding interactions. In this work, we describe for the first time a deep learning system for generating 3D molecular structures conditioned on a receptor binding site. We approach the problem using a conditional variational autoencoder trained on an atomic density grid representation of cross-docked protein-ligand structures. We apply atom fitting and bond inference procedures to construct valid molecular conformations from generated atomic densities. We evaluate the properties of the generated molecules and demonstrate that they change significantly when conditioned on mutated receptors. We also explore the latent space learned by our generative model using sampling and interpolation techniques. This work opens the door for end-to-end prediction of stable bioactive molecules from protein structures with deep learning.
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Affiliation(s)
- Matthew Ragoza
- Intelligent Systems Program, University of Pittsburgh Pittsburgh PA 15213 USA
| | - Tomohide Masuda
- Department of Computational and Systems Biology, University of Pittsburgh Pittsburgh PA 15213 USA
| | - David Ryan Koes
- Department of Computational and Systems Biology, University of Pittsburgh Pittsburgh PA 15213 USA
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15
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Wang S, Song T, Zhang S, Jiang M, Wei Z, Li Z. Molecular substructure tree generative model for de novo drug design. Brief Bioinform 2022; 23:6510156. [PMID: 35039853 DOI: 10.1093/bib/bbab592] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 12/19/2021] [Accepted: 12/19/2021] [Indexed: 01/19/2023] Open
Abstract
Deep learning shortens the cycle of the drug discovery for its success in extracting features of molecules and proteins. Generating new molecules with deep learning methods could enlarge the molecule space and obtain molecules with specific properties. However, it is also a challenging task considering that the connections between atoms are constrained by chemical rules. Aiming at generating and optimizing new valid molecules, this article proposed Molecular Substructure Tree Generative Model, in which the molecule is generated by adding substructure gradually. The proposed model is based on the Variational Auto-Encoder architecture, which uses the encoder to map molecules to the latent vector space, and then builds an autoregressive generative model as a decoder to generate new molecules from Gaussian distribution. At the same time, for the molecular optimization task, a molecular optimization model based on CycleGAN was constructed. Experiments showed that the model could generate valid and novel molecules, and the optimized model effectively improves the molecular properties.
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Affiliation(s)
- Shuang Wang
- College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China
| | - Tao Song
- College of Computer Science and Technology, China University of Petroleum (East China), Qingdao 266580, China
| | - Shugang Zhang
- College of Computer Science and Technology, Ocean University of China, Qingdao 266100, China
| | - Mingjian Jiang
- School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266033, China
| | - Zhiqiang Wei
- College of Computer Science and Technology, Ocean University of China, Qingdao 266100, China
| | - Zhen Li
- College of Computer Science and Technology, Qingdao University, Qingdao 266071, China
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16
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Kwon Y, Kang S, Choi YS, Kim I. Evolutionary design of molecules based on deep learning and a genetic algorithm. Sci Rep 2021; 11:17304. [PMID: 34453086 PMCID: PMC8397714 DOI: 10.1038/s41598-021-96812-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2020] [Accepted: 08/17/2021] [Indexed: 11/09/2022] Open
Abstract
Evolutionary design has gained significant attention as a useful tool to accelerate the design process by automatically modifying molecular structures to obtain molecules with the target properties. However, its methodology presents a practical challenge-devising a way in which to rapidly evolve molecules while maintaining their chemical validity. In this study, we address this limitation by developing an evolutionary design method. The method employs deep learning models to extract the inherent knowledge from a database of materials and is used to effectively guide the evolutionary design. In the proposed method, the Morgan fingerprint vectors of seed molecules are evolved using the techniques of mutation and crossover within the genetic algorithm. Then, a recurrent neural network is used to reconstruct the final fingerprints into actual molecular structures while maintaining their chemical validity. The use of deep neural network models to predict the properties of these molecules enabled more versatile and efficient molecular evaluations to be conducted by using the proposed method repeatedly. Four design tasks were performed to modify the light-absorbing wavelengths of organic molecules from the PubChem library.
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Affiliation(s)
- Youngchun Kwon
- Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do, 16678, Republic of Korea
| | - Seokho Kang
- Department of Industrial Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon-si, Gyeonggi-do, 16419, Republic of Korea
| | - Youn-Suk Choi
- Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do, 16678, Republic of Korea.
| | - Inkoo Kim
- Data and Information Technology Center, Samsung Electronics Co. Ltd., 1-2 Samsungjeonja-ro, Hwaseong-si, Gyeonggi-do, 18448, Republic of Korea
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17
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Grant LL, Sit CS. De novo molecular drug design benchmarking. RSC Med Chem 2021; 12:1273-1280. [PMID: 34458735 DOI: 10.1039/d1md00074h] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Accepted: 05/24/2021] [Indexed: 11/21/2022] Open
Abstract
De novo molecular design for drug discovery is a growing field. Deep neural networks (DNNs) are becoming more widespread in their use for machine learning models. As more DNN models are proposed for molecular design, benchmarking methods are crucial for the comparision and validation of these models. This review looks at recently proposed benchmarking methods Fréchet ChemNet Distance, GuacaMol and Molecular Sets (MOSES), and provides a commentary on their future potential applications in de novo molecular drug design and possible next steps for further validation of these benchmarking methods.
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18
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Lovrić M, Đuričić T, Tran HTN, Hussain H, Lacić E, Rasmussen MA, Kern R. Should We Embed in Chemistry? A Comparison of Unsupervised Transfer Learning with PCA, UMAP, and VAE on Molecular Fingerprints. Pharmaceuticals (Basel) 2021; 14:758. [PMID: 34451855 PMCID: PMC8400160 DOI: 10.3390/ph14080758] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 07/21/2021] [Accepted: 07/22/2021] [Indexed: 02/07/2023] Open
Abstract
Methods for dimensionality reduction are showing significant contributions to knowledge generation in high-dimensional modeling scenarios throughout many disciplines. By achieving a lower dimensional representation (also called embedding), fewer computing resources are needed in downstream machine learning tasks, thus leading to a faster training time, lower complexity, and statistical flexibility. In this work, we investigate the utility of three prominent unsupervised embedding techniques (principal component analysis-PCA, uniform manifold approximation and projection-UMAP, and variational autoencoders-VAEs) for solving classification tasks in the domain of toxicology. To this end, we compare these embedding techniques against a set of molecular fingerprint-based models that do not utilize additional pre-preprocessing of features. Inspired by the success of transfer learning in several fields, we further study the performance of embedders when trained on an external dataset of chemical compounds. To gain a better understanding of their characteristics, we evaluate the embedders with different embedding dimensionalities, and with different sizes of the external dataset. Our findings show that the recently popularized UMAP approach can be utilized alongside known techniques such as PCA and VAE as a pre-compression technique in the toxicology domain. Nevertheless, the generative model of VAE shows an advantage in pre-compressing the data with respect to classification accuracy.
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Affiliation(s)
- Mario Lovrić
- Know-Center, Inffeldgasse 13, 8010 Graz, Austria; (M.L.); (T.Đ.); (H.T.N.T.); (H.H.); (E.L.)
- Centre for Applied Bioanthropology, Institute for Anthropological Research, 10000 Zagreb, Croatia
| | - Tomislav Đuričić
- Know-Center, Inffeldgasse 13, 8010 Graz, Austria; (M.L.); (T.Đ.); (H.T.N.T.); (H.H.); (E.L.)
- Institute of Interactive Systems and Data Science, Graz University of Technology, Inffeldgasse 16C, 8010 Graz, Austria
| | - Han T. N. Tran
- Know-Center, Inffeldgasse 13, 8010 Graz, Austria; (M.L.); (T.Đ.); (H.T.N.T.); (H.H.); (E.L.)
| | - Hussain Hussain
- Know-Center, Inffeldgasse 13, 8010 Graz, Austria; (M.L.); (T.Đ.); (H.T.N.T.); (H.H.); (E.L.)
- Institute of Interactive Systems and Data Science, Graz University of Technology, Inffeldgasse 16C, 8010 Graz, Austria
| | - Emanuel Lacić
- Know-Center, Inffeldgasse 13, 8010 Graz, Austria; (M.L.); (T.Đ.); (H.T.N.T.); (H.H.); (E.L.)
| | - Morten A. Rasmussen
- Copenhagen Studies on Asthma in Childhood, Herlev-Gentofte Hospital, University of Copenhagen, Ledreborg Alle 34, 2820 Gentofte, Denmark;
- Department of Food Science, University of Copenhagen, Rolighedsvej 26, 1958 Frederiksberg, Denmark
| | - Roman Kern
- Know-Center, Inffeldgasse 13, 8010 Graz, Austria; (M.L.); (T.Đ.); (H.T.N.T.); (H.H.); (E.L.)
- Institute of Interactive Systems and Data Science, Graz University of Technology, Inffeldgasse 16C, 8010 Graz, Austria
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19
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Zhang XM, Liang L, Liu L, Tang MJ. Graph Neural Networks and Their Current Applications in Bioinformatics. Front Genet 2021; 12:690049. [PMID: 34394185 PMCID: PMC8360394 DOI: 10.3389/fgene.2021.690049] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 05/28/2021] [Indexed: 12/22/2022] Open
Abstract
Graph neural networks (GNNs), as a branch of deep learning in non-Euclidean space, perform particularly well in various tasks that process graph structure data. With the rapid accumulation of biological network data, GNNs have also become an important tool in bioinformatics. In this research, a systematic survey of GNNs and their advances in bioinformatics is presented from multiple perspectives. We first introduce some commonly used GNN models and their basic principles. Then, three representative tasks are proposed based on the three levels of structural information that can be learned by GNNs: node classification, link prediction, and graph generation. Meanwhile, according to the specific applications for various omics data, we categorize and discuss the related studies in three aspects: disease prediction, drug discovery, and biomedical imaging. Based on the analysis, we provide an outlook on the shortcomings of current studies and point out their developing prospect. Although GNNs have achieved excellent results in many biological tasks at present, they still face challenges in terms of low-quality data processing, methodology, and interpretability and have a long road ahead. We believe that GNNs are potentially an excellent method that solves various biological problems in bioinformatics research.
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Affiliation(s)
- Xiao-Meng Zhang
- School of Information, Yunnan Normal University, Kunming, China
| | - Li Liang
- School of Information, Yunnan Normal University, Kunming, China
| | - Lin Liu
- School of Information, Yunnan Normal University, Kunming, China
- Key Laboratory of Educational Informatization for Nationalities Ministry of Education, Yunnan Normal University, Kunming, China
| | - Ming-Jing Tang
- Key Laboratory of Educational Informatization for Nationalities Ministry of Education, Yunnan Normal University, Kunming, China
- School of Life Sciences, Yunnan Normal University, Kunming, China
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20
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Mahmood O, Mansimov E, Bonneau R, Cho K. Masked graph modeling for molecule generation. Nat Commun 2021; 12:3156. [PMID: 34039973 PMCID: PMC8155025 DOI: 10.1038/s41467-021-23415-2] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 04/28/2021] [Indexed: 11/30/2022] Open
Abstract
De novo, in-silico design of molecules is a challenging problem with applications in drug discovery and material design. We introduce a masked graph model, which learns a distribution over graphs by capturing conditional distributions over unobserved nodes (atoms) and edges (bonds) given observed ones. We train and then sample from our model by iteratively masking and replacing different parts of initialized graphs. We evaluate our approach on the QM9 and ChEMBL datasets using the GuacaMol distribution-learning benchmark. We find that validity, KL-divergence and Fréchet ChemNet Distance scores are anti-correlated with novelty, and that we can trade off between these metrics more effectively than existing models. On distributional metrics, our model outperforms previously proposed graph-based approaches and is competitive with SMILES-based approaches. Finally, we show our model generates molecules with desired values of specified properties while maintaining physiochemical similarity to the training distribution.
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Affiliation(s)
- Omar Mahmood
- Center for Data Science, New York University, New York, NY, USA
| | - Elman Mansimov
- Department of Computer Science, Courant Institute of Mathematical Sciences, New York, NY, USA
| | - Richard Bonneau
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
| | - Kyunghyun Cho
- Department of Computer Science, Courant Institute of Mathematical Sciences, New York, NY, USA.
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21
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Kwon Y, Lee J. MolFinder: an evolutionary algorithm for the global optimization of molecular properties and the extensive exploration of chemical space using SMILES. J Cheminform 2021; 13:24. [PMID: 33736687 PMCID: PMC7977239 DOI: 10.1186/s13321-021-00501-7] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Accepted: 02/27/2021] [Indexed: 12/22/2022] Open
Abstract
Here, we introduce a new molecule optimization method, MolFinder, based on an efficient global optimization algorithm, the conformational space annealing algorithm, and the SMILES representation. MolFinder finds diverse molecules with desired properties efficiently without any training and a large molecular database. Compared with recently proposed reinforcement-learning-based molecule optimization algorithms, MolFinder consistently outperforms in terms of both the optimization of a given target property and the generation of a set of diverse and novel molecules. The efficiency of MolFinder demonstrates that combinatorial optimization using the SMILES representation is a promising approach for molecule optimization, which has not been well investigated despite its simplicity. We believe that our results shed light on new possibilities for advances in molecule optimization methods.
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Affiliation(s)
- Yongbeom Kwon
- Department of Chemistry, Division of Chemistry and Biochemistry, Kangwon National University, 1 Gangwondaehak-gil, Chuncheon, 24341, Republic of Korea.,Arontier Inc., 15F, 241, Gangnam-daero, Seocho-gu, Seoul, 06735, Republic of Korea
| | - Juyong Lee
- Department of Chemistry, Division of Chemistry and Biochemistry, Kangwon National University, 1 Gangwondaehak-gil, Chuncheon, 24341, Republic of Korea.
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22
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Lim S, Lu Y, Cho CY, Sung I, Kim J, Kim Y, Park S, Kim S. A review on compound-protein interaction prediction methods: Data, format, representation and model. Comput Struct Biotechnol J 2021; 19:1541-1556. [PMID: 33841755 PMCID: PMC8008185 DOI: 10.1016/j.csbj.2021.03.004] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 02/28/2021] [Accepted: 03/01/2021] [Indexed: 01/27/2023] Open
Abstract
There has recently been a rapid progress in computational methods for determining protein targets of small molecule drugs, which will be termed as compound protein interaction (CPI). In this review, we comprehensively review topics related to computational prediction of CPI. Data for CPI has been accumulated and curated significantly both in quantity and quality. Computational methods have become powerful ever to analyze such complex the data. Thus, recent successes in the improved quality of CPI prediction are due to use of both sophisticated computational techniques and higher quality information in the databases. The goal of this article is to provide reviews of topics related to CPI, such as data, format, representation, to computational models, so that researchers can take full advantages of these resources to develop novel prediction methods. Chemical compounds and protein data from various resources were discussed in terms of data formats and encoding schemes. For the CPI methods, we grouped prediction methods into five categories from traditional machine learning techniques to state-of-the-art deep learning techniques. In closing, we discussed emerging machine learning topics to help both experimental and computational scientists leverage the current knowledge and strategies to develop more powerful and accurate CPI prediction methods.
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Affiliation(s)
- Sangsoo Lim
- Bioinformatics Institute, Seoul National University, Seoul, Republic of Korea
| | - Yijingxiu Lu
- Department of Computer Science and Engineering, College of Engineering, Seoul National University, Seoul, Republic of Korea
| | - Chang Yun Cho
- Institute of Engineering Research, Seoul National University, Seoul, Republic of Korea
| | - Inyoung Sung
- Institute of Engineering Research, Seoul National University, Seoul, Republic of Korea
| | - Jungwoo Kim
- Department of Computer Science and Engineering, College of Engineering, Seoul National University, Seoul, Republic of Korea
| | - Youngkuk Kim
- Department of Computer Science and Engineering, College of Engineering, Seoul National University, Seoul, Republic of Korea
| | - Sungjoon Park
- Department of Computer Science and Engineering, College of Engineering, Seoul National University, Seoul, Republic of Korea
| | - Sun Kim
- Bioinformatics Institute, Seoul National University, Seoul, Republic of Korea
- Department of Computer Science and Engineering, College of Engineering, Seoul National University, Seoul, Republic of Korea
- Institute of Engineering Research, Seoul National University, Seoul, Republic of Korea
- Interdisciplinary Program in Bioinformatics, College of Natural Sciences, Seoul National University, Seoul, Republic of Korea
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23
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Mercado R, Rastemo T, Lindelöf E, Klambauer G, Engkvist O, Chen H, Jannik Bjerrum E. Graph networks for molecular design. MACHINE LEARNING-SCIENCE AND TECHNOLOGY 2021. [DOI: 10.1088/2632-2153/abcf91] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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24
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Graph neural networks for automated de novo drug design. Drug Discov Today 2021; 26:1382-1393. [PMID: 33609779 DOI: 10.1016/j.drudis.2021.02.011] [Citation(s) in RCA: 47] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 01/27/2021] [Accepted: 02/11/2021] [Indexed: 01/10/2023]
Abstract
The goal of de novo drug design is to create novel chemical entities with desired biological activities and pharmacokinetics (PK) properties. Over recent years, with the development of artificial intelligence (AI) technologies, data-driven methods have rapidly gained in popularity in this field. Among them, graph neural networks (GNNs), a type of neural network directly operating on the graph structure data, have received extensive attention. In this review, we introduce the applications of GNNs in de novo drug design from three aspects: molecule scoring, molecule generation and optimization, and synthesis planning. Furthermore, we also discuss the current challenges and future directions of GNNs in de novo drug design.
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25
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Mercado R, Rastemo T, Lindelöf E, Klambauer G, Engkvist O, Chen H, Bjerrum EJ. Practical notes on building molecular graph generative models. ACTA ACUST UNITED AC 2021. [DOI: 10.1002/ail2.18] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Affiliation(s)
- Rocío Mercado
- Molecular AI, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca Gothenburg Sweden
| | - Tobias Rastemo
- Molecular AI, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca Gothenburg Sweden
- Chalmers University of Technology Gothenburg Sweden
| | - Edvard Lindelöf
- Molecular AI, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca Gothenburg Sweden
- Chalmers University of Technology Gothenburg Sweden
| | - Günter Klambauer
- Institute of Bioinformatics, Johannes Kepler University Linz Austria
| | - Ola Engkvist
- Molecular AI, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca Gothenburg Sweden
| | - Hongming Chen
- Centre of Chemistry and Chemical Biology, Guangzhou Regenerative Medicine and Health, Guangdong Laboratory Guangzhou China
| | - Esben Jannik Bjerrum
- Molecular AI, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca Gothenburg Sweden
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26
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Kim H, Kim E, Lee I, Bae B, Park M, Nam H. Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches. BIOTECHNOL BIOPROC E 2021; 25:895-930. [PMID: 33437151 PMCID: PMC7790479 DOI: 10.1007/s12257-020-0049-y] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Revised: 05/27/2020] [Accepted: 06/03/2020] [Indexed: 02/07/2023]
Abstract
As expenditure on drug development increases exponentially, the overall drug discovery process requires a sustainable revolution. Since artificial intelligence (AI) is leading the fourth industrial revolution, AI can be considered as a viable solution for unstable drug research and development. Generally, AI is applied to fields with sufficient data such as computer vision and natural language processing, but there are many efforts to revolutionize the existing drug discovery process by applying AI. This review provides a comprehensive, organized summary of the recent research trends in AI-guided drug discovery process including target identification, hit identification, ADMET prediction, lead optimization, and drug repositioning. The main data sources in each field are also summarized in this review. In addition, an in-depth analysis of the remaining challenges and limitations will be provided, and proposals for promising future directions in each of the aforementioned areas.
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Affiliation(s)
- Hyunho Kim
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005 Korea
| | - Eunyoung Kim
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005 Korea
| | - Ingoo Lee
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005 Korea
| | - Bongsung Bae
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005 Korea
| | - Minsu Park
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005 Korea
| | - Hojung Nam
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005 Korea
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27
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Kwon Y, Lee D, Choi YS, Shin K, Kang S. Compressed graph representation for scalable molecular graph generation. J Cheminform 2020; 12:58. [PMID: 33431050 PMCID: PMC7513488 DOI: 10.1186/s13321-020-00463-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Accepted: 09/17/2020] [Indexed: 11/22/2022] Open
Abstract
Recently, deep learning has been successfully applied to molecular graph generation. Nevertheless, mitigating the computational complexity, which increases with the number of nodes in a graph, has been a major challenge. This has hindered the application of deep learning-based molecular graph generation to large molecules with many heavy atoms. In this study, we present a molecular graph compression method to alleviate the complexity while maintaining the capability of generating chemically valid and diverse molecular graphs. We designate six small substructural patterns that are prevalent between two atoms in real-world molecules. These relevant substructures in a molecular graph are then converted to edges by regarding them as additional edge features along with the bond types. This reduces the number of nodes significantly without any information loss. Consequently, a generative model can be constructed in a more efficient and scalable manner with large molecules on a compressed graph representation. We demonstrate the effectiveness of the proposed method for molecules with up to 88 heavy atoms using the GuacaMol benchmark.
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Affiliation(s)
- Youngchun Kwon
- Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon, Republic of Korea.,Department of Computer Science and Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, Republic of Korea
| | - Dongseon Lee
- Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon, Republic of Korea
| | - Youn-Suk Choi
- Samsung Advanced Institute of Technology, Samsung Electronics Co. Ltd., 130 Samsung-ro, Yeongtong-gu, Suwon, Republic of Korea.
| | - Kyoham Shin
- Department of Industrial Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon, Republic of Korea
| | - Seokho Kang
- Department of Industrial Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon, Republic of Korea.
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28
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Leguy J, Cauchy T, Glavatskikh M, Duval B, Da Mota B. EvoMol: a flexible and interpretable evolutionary algorithm for unbiased de novo molecular generation. J Cheminform 2020; 12:55. [PMID: 33431049 PMCID: PMC7494000 DOI: 10.1186/s13321-020-00458-z] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 08/31/2020] [Indexed: 11/24/2022] Open
Abstract
The objective of this work is to design a molecular generator capable of exploring known as well as unfamiliar areas of the chemical space. Our method must be flexible to adapt to very different problems. Therefore, it has to be able to work with or without the influence of prior data and knowledge. Moreover, regardless of the success, it should be as interpretable as possible to allow for diagnosis and improvement. We propose here a new open source generation method using an evolutionary algorithm to sequentially build molecular graphs. It is independent of starting data and can generate totally unseen compounds. To be able to search a large part of the chemical space, we define an original set of 7 generic mutations close to the atomic level. Our method achieves excellent performances and even records on the QED, penalised logP, SAscore, CLscore as well as the set of goal-directed functions defined in GuacaMol. To demonstrate its flexibility, we tackle a very different objective issued from the organic molecular materials domain. We show that EvoMol can generate sets of optimised molecules having high energy HOMO or low energy LUMO, starting only from methane. We can also set constraints on a synthesizability score and structural features. Finally, the interpretability of EvoMol allows for the visualisation of its exploration process as a chemically relevant tree. ![]()
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Affiliation(s)
- Jules Leguy
- Laboratoire LERIA, UNIV Angers, SFR MathSTIC, 2 Bd Lavoisier, 49045, Angers, France
| | - Thomas Cauchy
- Laboratoire MOLTECH-Anjou, UMR CNRS 6200, UNIV Angers, SFR MATRIX, 2 Bd Lavoisier, 49045, Angers, France.
| | - Marta Glavatskikh
- Laboratoire LERIA, UNIV Angers, SFR MathSTIC, 2 Bd Lavoisier, 49045, Angers, France.,Laboratoire MOLTECH-Anjou, UMR CNRS 6200, UNIV Angers, SFR MATRIX, 2 Bd Lavoisier, 49045, Angers, France
| | - Béatrice Duval
- Laboratoire LERIA, UNIV Angers, SFR MathSTIC, 2 Bd Lavoisier, 49045, Angers, France
| | - Benoit Da Mota
- Laboratoire LERIA, UNIV Angers, SFR MathSTIC, 2 Bd Lavoisier, 49045, Angers, France.
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29
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A gentle introduction to deep learning for graphs. Neural Netw 2020; 129:203-221. [PMID: 32559609 DOI: 10.1016/j.neunet.2020.06.006] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2019] [Revised: 04/20/2020] [Accepted: 06/04/2020] [Indexed: 12/29/2022]
Abstract
The adaptive processing of graph data is a long-standing research topic that has been lately consolidated as a theme of major interest in the deep learning community. The snap increase in the amount and breadth of related research has come at the price of little systematization of knowledge and attention to earlier literature. This work is a tutorial introduction to the field of deep learning for graphs. It favors a consistent and progressive presentation of the main concepts and architectural aspects over an exposition of the most recent literature, for which the reader is referred to available surveys. The paper takes a top-down view of the problem, introducing a generalized formulation of graph representation learning based on a local and iterative approach to structured information processing. Moreover, it introduces the basic building blocks that can be combined to design novel and effective neural models for graphs. We complement the methodological exposition with a discussion of interesting research challenges and applications in the field.
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30
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Li X, Xu Y, Yao H, Lin K. Chemical space exploration based on recurrent neural networks: applications in discovering kinase inhibitors. J Cheminform 2020; 12:42. [PMID: 33430983 PMCID: PMC7278228 DOI: 10.1186/s13321-020-00446-3] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Accepted: 06/04/2020] [Indexed: 01/10/2023] Open
Abstract
With the rise of artificial intelligence (AI) in drug discovery, de novo molecular generation provides new ways to explore chemical space. However, because de novo molecular generation methods rely on abundant known molecules, generated molecules may have a problem of novelty. Novelty is important in highly competitive areas of medicinal chemistry, such as the discovery of kinase inhibitors. In this study, de novo molecular generation based on recurrent neural networks was applied to discover a new chemical space of kinase inhibitors. During the application, the practicality was evaluated, and new inspiration was found. With the successful discovery of one potent Pim1 inhibitor and two lead compounds that inhibit CDK4, AI-based molecular generation shows potentials in drug discovery and development.![]()
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Affiliation(s)
- Xuanyi Li
- Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing, 210009, China
| | - Yinqiu Xu
- Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing, 210009, China
| | - Hequan Yao
- Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing, 210009, China.
| | - Kejiang Lin
- Department of Medicinal Chemistry, School of Pharmacy, China Pharmaceutical University, 24 Tongjiaxiang, Nanjing, 210009, China.
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Lim J, Hwang SY, Moon S, Kim S, Kim WY. Scaffold-based molecular design with a graph generative model. Chem Sci 2019; 11:1153-1164. [PMID: 34084372 PMCID: PMC8146476 DOI: 10.1039/c9sc04503a] [Citation(s) in RCA: 67] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Accepted: 12/03/2019] [Indexed: 01/02/2023] Open
Abstract
Searching for new molecules in areas like drug discovery often starts from the core structures of known molecules. Such a method has called for a strategy of designing derivative compounds retaining a particular scaffold as a substructure. On this account, our present work proposes a graph generative model that targets its use in scaffold-based molecular design. Our model accepts a molecular scaffold as input and extends it by sequentially adding atoms and bonds. The generated molecules are then guaranteed to contain the scaffold with certainty, and their properties can be controlled by conditioning the generation process on desired properties. The learned rule of extending molecules can well generalize to arbitrary kinds of scaffolds, including those unseen during learning. In the conditional generation of molecules, our model can simultaneously control multiple chemical properties despite the search space constrained by fixing the substructure. As a demonstration, we applied our model to designing inhibitors of the epidermal growth factor receptor and show that our model can employ a simple semi-supervised extension to broaden its applicability to situations where only a small amount of data is available.
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Affiliation(s)
- Jaechang Lim
- Department of Chemistry, KAIST 291 Daehak-ro, Yuseong-gu Daejeon 34141 Republic of Korea
| | - Sang-Yeon Hwang
- Department of Chemistry, KAIST 291 Daehak-ro, Yuseong-gu Daejeon 34141 Republic of Korea
| | - Seokhyun Moon
- Department of Chemistry, KAIST 291 Daehak-ro, Yuseong-gu Daejeon 34141 Republic of Korea
| | - Seungsu Kim
- School of Computing, KAIST 291 Daehak-ro, Yuseong-gu Daejeon 34141 Republic of Korea
| | - Woo Youn Kim
- Department of Chemistry, KAIST 291 Daehak-ro, Yuseong-gu Daejeon 34141 Republic of Korea
- KI for Artificial Intelligence, KAIST 291 Daehak-ro, Yuseong-gu Daejeon 34141 Republic of Korea
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Graph-based generative models for de Novo drug design. DRUG DISCOVERY TODAY. TECHNOLOGIES 2019; 32-33:45-53. [PMID: 33386094 DOI: 10.1016/j.ddtec.2020.11.004] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Revised: 10/29/2020] [Accepted: 11/05/2020] [Indexed: 12/13/2022]
Abstract
The discovery of new chemical entities is a crucial part of drug discovery, which requires the lead compounds to have desired properties to be pharmaceutically active. De novo drug design aims to generate and optimize novel ligands for macromolecular targets from scratch. The development of graph-based deep generative neural networks has provided a new method. In this review, we gave a brief introduction to graph representation and graph-based generative models for de novo drug design, summarized them as four architectures, and concluded each's characteristics. We also discussed generative models for scaffold- and fragment-based design and graph-based generative models' future directions.
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