Abstract — The ability to rank molecules according to their effectiveness in some domain, e.g. pesticide, drug, is important due to the cost of synthesizing and testing chemical compounds. Virtual screening seeks to do this computationally with potential savings of millions of pounds and large profits associated with reduced time to market. A current leading machine learning algorithm in this area – Binary kernel discrimination produces scores based on the estimated likelihood ratio of active to inactive compounds that are then ranked. As the prediction by posterior estimation is effective for noisy high dimensional data, this paper aims to estimate, directly the posterior probability of molecules being active and rank the molecules based o...
This paper discusses the use of a machine-learning technique called binary kernel discrimination (BK...
Le processus de découverte de médicaments a un succès limité malgré tous les progrès réalisés. En ef...
Virtual Screening (VS) methods can considerably aid clinical research, predicting how ligands intera...
The ability to rank molecules according to their effectiveness in some domain, e.g. pesticide, drug,...
This thesis lies in the area of chemoinformatics, known as virtual screening (VS). VS describes a se...
This thesis lies in the area of chemoinformatics, known as virtual screening (VS). VS describes a se...
© 2020 by the authors. Pharmacophore modeling is usually considered as a special type of virtual scr...
We investigate the utility of modern kernel-based machine learning methods for ligand-based virtual ...
Machine-learning methods can be used for virtual screening by analysing the structural characteristi...
Binary kernel discrimination (BKD) uses a training set of compounds, for which structural and qualit...
Virtual screening (VS) methods can be categorized into structure-based virtual screening (SBVS) that...
The high-throughput technologies of combinatorial chemistry and high-throughput screening have cause...
Virtual screening is an important step in early-phase of drug discovery process. Since there are tho...
The central idea of supervised classification in chemoinformatics is to design a classifying algorit...
Virtual screening is an important step in early-phase of drug discovery process. Since there are tho...
This paper discusses the use of a machine-learning technique called binary kernel discrimination (BK...
Le processus de découverte de médicaments a un succès limité malgré tous les progrès réalisés. En ef...
Virtual Screening (VS) methods can considerably aid clinical research, predicting how ligands intera...
The ability to rank molecules according to their effectiveness in some domain, e.g. pesticide, drug,...
This thesis lies in the area of chemoinformatics, known as virtual screening (VS). VS describes a se...
This thesis lies in the area of chemoinformatics, known as virtual screening (VS). VS describes a se...
© 2020 by the authors. Pharmacophore modeling is usually considered as a special type of virtual scr...
We investigate the utility of modern kernel-based machine learning methods for ligand-based virtual ...
Machine-learning methods can be used for virtual screening by analysing the structural characteristi...
Binary kernel discrimination (BKD) uses a training set of compounds, for which structural and qualit...
Virtual screening (VS) methods can be categorized into structure-based virtual screening (SBVS) that...
The high-throughput technologies of combinatorial chemistry and high-throughput screening have cause...
Virtual screening is an important step in early-phase of drug discovery process. Since there are tho...
The central idea of supervised classification in chemoinformatics is to design a classifying algorit...
Virtual screening is an important step in early-phase of drug discovery process. Since there are tho...
This paper discusses the use of a machine-learning technique called binary kernel discrimination (BK...
Le processus de découverte de médicaments a un succès limité malgré tous les progrès réalisés. En ef...
Virtual Screening (VS) methods can considerably aid clinical research, predicting how ligands intera...