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Drug Discovery: Overview

Drug Discovery: Overview

Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...

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Updated: Jan 7, 2026

Development of Inhibitors of Protein-protein Interactions through REPLACE: Application to the Design and Development Non-ATP Competitive CDK Inhibitors

Development of Inhibitors of Protein-protein Interactions through REPLACE: Application to the Design and Development Non-ATP Competitive CDK Inhibitors

Published on: October 26, 2015

In silico-driven protocol for hit-to-lead optimization: a case study on PDE9A inhibitors.

Hiroyuki Ogawa^1,2, Masateru Ohta³, Mitsunori Ikeguchi^4,5

¹Graduate School of Medical Life Science, Yokohama City University, 1-7-29 Suehiro-cho, Tsurumi-ku, Yokohama, 230-0045, Japan.

Journal of Computer-Aided Molecular Design

|December 18, 2025

Summary

This summary is machine-generated.

This study introduces an in silico hit-to-lead (H2L) optimization protocol. It uses molecular generation, non-equilibrium switching (NES) for binding affinity, and machine learning (ML) for ADME properties to explore chemical space efficiently.

Keywords:

Drug discovery Hit-to-lead Non-equilibrium switching PDE9A

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Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery

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Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery

Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery

Published on: May 16, 2021

Area of Science:

Computational chemistry
Drug discovery
Medicinal chemistry

Background:

Hit-to-lead (H2L) optimization is crucial for small-molecule drug discovery.
Traditional H2L methods limit chemical space exploration due to iterative synthesis and evaluation.
In silico approaches offer efficient exploration of vast chemical spaces via virtual compound generation and computational evaluation.

Purpose of the Study:

To develop and validate an in silico-driven H2L protocol for efficient chemical space exploration.
To assess the accuracy and utility of the non-equilibrium switching (NES) method for binding free energy calculations in H2L.
To integrate molecular generation, NES-based affinity prediction, and machine learning (ML) for ADME property evaluation.

Main Methods:

Developed an integrated in silico H2L protocol combining molecular generation, NES for relative binding free energy calculations, and ML for ADME property prediction (solubility, metabolic stability, permeability).
Applied the protocol to a phosphodiesterase 9A inhibitor model system, starting from a high-throughput screening hit.
Conducted large-scale exploration of substituent space at two key positions.

Main Results:

The in silico protocol successfully identified compounds with high predicted binding affinity and favorable ADME properties.
The lead compound previously reported in the literature was identified among the top-ranked candidates.
NES method demonstrated effectiveness in large-scale substituent space exploration for H2L optimization.

Conclusions:

An in silico H2L protocol integrating large-scale molecular generation, high-accuracy NES affinity prediction, and ML-based ADME prediction enables broader exploration of substituent space.
This approach accelerates the identification of promising drug candidates with desirable properties.
The study validates the utility of NES for precise binding free energy calculations in drug discovery workflows.