AlvaBuilder: A Software for De Novo Molecular Design

Abstract

AlvaBuilder is a software tool for de novo molecular design and can be used to generate novel molecules having desirable characteristics. Such characteristics can be defined using a simple step by step graphical interface, and they can be based on molecular descriptors, on predictions of QSAR/QSPR models, and on matching molecular fragments or used to design compounds similar to a given one. The molecules generated are always syntactically valid since they are composed by combining fragments of molecules taken from a training data set chosen by the user. In this paper, we demonstrate how the software can be used to design new compounds for a defined case study. AlvaBuilder is available at

Consensus docking aid to model the activity of an inhibitor of DNA methyltransferase 1 inspired by de novo design

“…Automated de novo design was carried out with alvaBuilder v.1.0.6 (Mauri and Bertola, 2023). Briefly, alvaBuilder combines structural fragments which are obtained from the training sets chosen by the user.…”

Streamlining Computational Fragment-Based Drug Discovery through Evolutionary Optimization Informed by Ligand-Based Virtual Prescreening

“…Computational de novo drug design involves the use of techniques such as genetic algorithms, − reinforcement learning, including deep reinforcement learning, − generative deep learning models, − or other deep learning methods, e.g., graph transformers, , models that blend deep learning and evolutionary algorithms, ,, and string-based transformers (i.e., operating on a simplified molecular-input line-entry system (SMILES) string representation of molecules). , The algorithms “computationally synthesize” novel drug molecules, either by starting from scratch and adding atoms to form a novel molecule or by modifying or adding atoms to an existing chemical structure (“scaffold”). The result is the creation of novel molecules by (a) simulating chemical modifications that optimize for the single objective of improving binding efficiency to a target or (b) multiobjective optimization including drug-likeness objectives, e.g., solubility and other drug-likeness factors. ,− …”

“…Computational de novo drug design involves the use of techniques such as genetic algorithms, 24 − 29 reinforcement learning, including deep reinforcement learning, 30 − 34 generative deep learning models, 35 − 41 or other deep learning methods, e.g., graph transformers, 42 , 43 models that blend deep learning and evolutionary algorithms, 26 , 44 , 45 and string-based transformers (i.e., operating on a simplified molecular-input line-entry system (SMILES) string representation of molecules). 46 , 47 The algorithms “computationally synthesize” novel drug molecules, either by starting from scratch and adding atoms to form a novel molecule or by modifying or adding atoms to an existing chemical structure (“scaffold”).…”

Streamlining Computational Fragment-Based Drug Discovery through Evolutionary Optimization Informed by Ligand-Based Virtual Prescreening

“…Computational de novo drug design involves the use of techniques such as genetic algorithms 3,39,47,51,54,73 , reinforcement learn-ing, including deep reinforcement learning 57,61,63,74,86 , generative deep learning models 5,6,9,35,41,43,55 , or other deep learning methods, e.g., graph transformers 46,71 , models that blend deep learning and evolutionary algorithms 1,26,54 , and string-based trans-formers (i.e. operating on a SMILES string representation of molecules) 27,33 .…”