Transfer and Multi-task Learning in QSAR Modeling: Advances and Challenges

Simões, Rodolfo S.; Maltarollo, Vinícius Gonçalves; Oliveira, Patrícia Rufino; Honório, Káthia Maria

doi:10.3389/fphar.2018.00074

Cited by 89 publications

(60 citation statements)

References 65 publications

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“…Transfer and multitask learning (TL and MTL) are two technologies that can make full use of the commonality of multiple tasks to build a more robust model . Combined with DL, they display a promising prospect in the development of novel ML‐based SFs.…”

Section: Deep Learning In Scoring Functionsmentioning

confidence: 99%

From machine learning to deep learning: Advances in scoring functions for protein–ligand docking

Shen

Ding

Wang

et al. 2019

WIREs Comput Mol Sci

186

180

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Molecule docking has been regarded as a routine tool for drug discovery, but its accuracy highly depends on the reliability of scoring functions (SFs). With the rapid development of machine learning (ML) techniques, ML-based SFs have gradually emerged as a promising alternative for protein-ligand binding affinity prediction and virtual screening, and most of them have shown significantly better performance than a wide range of classical SFs. Emergence of more data-hungry deep learning (DL) approaches in recent years further fascinates the exploitation of more accurate SFs. Here, we summarize the progress of traditional ML-based SFs in the last few years and provide insights into recently developed DL-based SFs. We believe that the continuous improvement in ML-based SFs can surely guide the early-stage drug design and accelerate the discovery of new drugs. This article is categorized under:Computer and Information Science > Chemoinformaticsdeep learning, machine learning, molecular docking, scoring function, structure-based drug design | INTRODUCTIONTraditional drug discovery largely relies on the application of high-throughput screening, an experimental technique with acceptable performance but high cost and low efficiency. 1 With the rapid development of computational chemistry and computer technology, computer-aided drug design (CADD) has gradually emerged as a powerful technique in the design and development of new drug candidates in the past three decades. 2 Virtual screening (VS), an important branch of CADD, can enrich potential actives from large virtual compound libraries through in silico methods rather than real experiments, which can not only accelerate the process of drug discovery but also greatly reduce the time and resource cost. 3-5 Depending on whether the three-dimensional (3D) structure of a target is used or not, VS approaches can be classified into two major categories: ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS). 6 LBVS aims to discover active molecules through the models developed based on a set of known ligands of a target of interest, which may limit its capability to find novel chemotypes. Compared with LBVS, SBVS is considered to be a better choice to discover novel active compounds if the 3D structure of a given target is available. 7 Chao Shen and Junjie Ding are equivalent first authors.

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Section: Deep Learning In Scoring Functionsmentioning

confidence: 99%

From machine learning to deep learning: Advances in scoring functions for protein–ligand docking

Shen

Ding

Wang

et al. 2019

WIREs Comput Mol Sci

186

180

View full text Add to dashboard Cite

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“…A similar computational concept has recently been pursued to yield membranolytic antimicrobial peptides, relying on extensive machine learning for activity prediction . In contrast, after training the computer model on generic peptide features, fine‐tuning by transfer learning with merely a small number of ACPs proved sufficient to obtain novel active ACPs in this present study. These computer‐generated amino acid sequences were maximally 27 % identical (56 % similarity) to the peptides from the fine‐tuning set, as determined by LALIGN .…”

Section: Figurementioning

confidence: 97%

Designing Anticancer Peptides by Constructive Machine Learning

Grisoni

Neuhaus²,

Gabernet³

et al. 2018

ChemMedChem

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Constructive (generative) machine learning enables the automated generation of novel chemical structures without the need for explicit molecular design rules. This study presents the experimental application of such a deep machine learning model to design membranolytic anticancer peptides (ACPs) de novo. A recurrent neural network with long short-term memory cells was trained on α-helical cationic amphipathic peptide sequences and then fine-tuned with 26 known ACPs by transfer learning. This optimized model was used to generate unique and novel amino acid sequences. Twelve of the peptides were synthesized and tested for their activity on MCF7 human breast adenocarcinoma cells and selectivity against human erythrocytes. Ten of these peptides were active against cancer cells. Six of the active peptides killed MCF7 cancer cells without affecting human erythrocytes with at least threefold selectivity. These results advocate constructive machine learning for the automated design of peptides with desired biological activities.

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“…These are extracted from recent contributions, that can be regarded as complementary and providing an overall perspective of the applications. These include different approaches for (i) understanding and controlling chemical systems and related behavior (Chakravarti, 2018;Fuchs et al, 2018;Janet et al, 2018;Elton et al, 2019;Mezei and Von Lilienfeld, 2019;Sanchez-Lengeling et al, 2019;Venkatasubramanian, 2019;Xu et al, 2019;Zhang et al, 2019), (ii) calculating, optimizing, or predicting structure-property relationships (Varnek and Baskin, 2012;Ramakrishnan et al, 2014;Goh et al, 2017;Simões et al, 2018;Chandrasekaran et al, 2019), density functional theory (DFT) functionals, and interatomic potentials (Snyder et al, 2012;Ramakrishnan et al, 2015;Faber et al, 2017;Hegde and Bowen, 2017;Smith et al, 2017;Pronobis et al, 2018;Mezei and Von Lilienfeld, 2019;Schleder et al, 2019), (iii) driving generative models for inverse design (i.e., produce stable molecules from a set of desired properties) (White and Wilson, 2010;Benjamin et al, 2017;Kadurin et al, 2017;Harel and Radinsky, 2018;Jørgensen et al, 2018b;Kang and Cho, 2018;Li et al, 2018b;Sanchez-Lengeling and Aspuru-Guzik, 2018;Schneider, 2018;Arús-Pous et al, 2019;Freeze et al, 2019;Jensen, 2019), (iv) screening, synthesizing, and characterizing new compounds and materials…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns

2019

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Computational Chemistry is currently a synergistic assembly between ab initio calculations, simulation, machine learning (ML) and optimization strategies for describing, solving and predicting chemical data and related phenomena. These include accelerated literature searches, analysis and prediction of physical and quantum chemical properties, transition states, chemical structures, chemical reactions, and also new catalysts and drug candidates. The generalization of scalability to larger chemical problems, rather than specialization, is now the main principle for transforming chemical tasks in multiple fronts, for which systematic and cost-effective solutions have benefited from ML approaches, including those based on deep learning (e.g. quantum chemistry, molecular screening, synthetic route design, catalysis, drug discovery). The latter class of ML algorithms is capable of combining raw input into layers of intermediate features, enabling bench-to-bytes designs with the potential to transform several chemical domains. In this review, the most exciting developments concerning the use of ML in a range of different chemical scenarios are described. A range of different chemical problems and respective rationalization, that have hitherto been inaccessible due to the lack of suitable analysis tools, is thus detailed, evidencing the breadth of potential applications of these emerging multidimensional approaches. Focus is given to the models, algorithms and methods proposed to facilitate research on compound design and synthesis, materials design, prediction of binding, molecular activity, and soft matter behavior. The information produced by pairing Chemistry and ML, through data-driven analyses, neural network predictions and monitoring of chemical systems, allows (i) prompting the ability to understand the complexity of chemical data, (ii) streamlining and designing experiments, (ii) discovering new molecular targets and materials, and also (iv) planning or rethinking forthcoming chemical challenges. In fact, optimization engulfs all these tasks directly.

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Transfer and Multi-task Learning in QSAR Modeling: Advances and Challenges

Cited by 89 publications

References 65 publications

From machine learning to deep learning: Advances in scoring functions for protein–ligand docking

From machine learning to deep learning: Advances in scoring functions for protein–ligand docking

Designing Anticancer Peptides by Constructive Machine Learning

Deep Learning for Deep Chemistry: Optimizing the Prediction of Chemical Patterns

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